Release the codes of PV-RCNN++, update OpenPCDet to v0.5.2

Release the codes of PV-RCNN++, update OpenPCDet to v0.5.2

README.md

@@ -4,9 +4,11 @@
 
 `OpenPCDet` is a clear, simple, self-contained open source project for LiDAR-based 3D object detection. 
 
-It is also the official code release of [`[PointRCNN]`](https://arxiv.org/abs/1812.04244), [`[Part-A2-Net]`](https://arxiv.org/abs/1907.03670), [`[PV-RCNN]`](https://arxiv.org/abs/1912.13192) and [`[Voxel R-CNN]`](https://arxiv.org/abs/2012.15712). 
+It is also the official code release of [`[PointRCNN]`](https://arxiv.org/abs/1812.04244), [`[Part-A2-Net]`](https://arxiv.org/abs/1907.03670), [`[PV-RCNN]`](https://arxiv.org/abs/1912.13192), [`[Voxel R-CNN]`](https://arxiv.org/abs/2012.15712) and [`[PV-RCNN++]`](https://arxiv.org/abs/2102.00463). 
 
-**NEW**: `OpenPCDet` has been updated to `v0.5.0` (Dec. 2021).
+**Highlights**: 
+* `OpenPCDet` has been updated to `v0.5.2` (Jan. 2022).
+* The codes of PV-RCNN++ has been supported.
 
 ## Overview
 - [Changelog](#changelog)
@@ -19,6 +21,12 @@ It is also the official code release of [`[PointRCNN]`](https://arxiv.org/abs/18
 
 
 ## Changelog
+[2022-01-05] **NEW:** Update `OpenPCDet` to v0.5.2:
+* The code of [PV-RCNN++](https://arxiv.org/abs/2102.00463) has been released to this repo, with higher performance, faster training/inference speed and less memory consumption than PV-RCNN.
+* Add performance of several models trained with full training set of [Waymo Open Dataset](#waymo-open-dataset-baselines).
+* Support Lyft dataset, see the pull request [here](https://github.com/open-mmlab/OpenPCDet/pull/720).
+
+
 [2021-12-09] **NEW:**  Update `OpenPCDet` to v0.5.1:
 * Add PointPillar related baseline configs/results on [Waymo Open Dataset](#waymo-open-dataset-baselines).
 * Support Pandaset dataloader, see the pull request [here](https://github.com/open-mmlab/OpenPCDet/pull/396).
@@ -108,7 +116,7 @@ Contributions are also welcomed.
 
 ### KITTI 3D Object Detection Baselines
 Selected supported methods are shown in the below table. The results are the 3D detection performance of moderate difficulty on the *val* set of KITTI dataset.
-* All models are trained with 8 GTX 1080Ti GPUs and are available for download. 
+* All LiDAR-based models are trained with 8 GTX 1080Ti GPUs and are available for download. 
 * The training time is measured with 8 TITAN XP GPUs and PyTorch 1.5.
 
 |                                             | training time | Car@R11 | Pedestrian@R11 | Cyclist@R11  | download | 
@@ -129,7 +137,7 @@ Selected supported methods are shown in the below table. The results are the 3D
 We provide the setting of [`DATA_CONFIG.SAMPLED_INTERVAL`](tools/cfgs/dataset_configs/waymo_dataset.yaml) on the Waymo Open Dataset (WOD) to subsample partial samples for training and evaluation, 
 so you could also play with WOD by setting a smaller `DATA_CONFIG.SAMPLED_INTERVAL` even if you only have limited GPU resources. 
 
-By default, all models are trained with **20% data (~32k frames)** of all the training samples on 8 GTX 1080Ti GPUs, and the results of each cell here are mAP/mAPH calculated by the official Waymo evaluation metrics on the **whole** validation set (version 1.2).    
+By default, all models are trained with **a single frame** of **20% data (~32k frames)** of all the training samples on 8 GTX 1080Ti GPUs, and the results of each cell here are mAP/mAPH calculated by the official Waymo evaluation metrics on the **whole** validation set (version 1.2).    
 
 |    Performance@(train with 20\% Data)            | Vec_L1 | Vec_L2 | Ped_L1 | Ped_L2 | Cyc_L1 | Cyc_L2 |  
 |---------------------------------------------|----------:|:-------:|:-------:|:-------:|:-------:|:-------:|
@@ -141,6 +149,22 @@ By default, all models are trained with **20% data (~32k frames)** of all the tr
 | [Part-A2-Anchor](tools/cfgs/waymo_models/PartA2.yaml) | 74.66/74.12	|65.82/65.32	|71.71/62.24	|62.46/54.06	|66.53/65.18	|64.05/62.75 |
 | [PV-RCNN (AnchorHead)](tools/cfgs/waymo_models/pv_rcnn.yaml) | 75.41/74.74	|67.44/66.80	|71.98/61.24	|63.70/53.95	|65.88/64.25	|63.39/61.82 | 
 | [PV-RCNN (CenterHead)](tools/cfgs/waymo_models/pv_rcnn_with_centerhead_rpn.yaml) | 75.95/75.43	|68.02/67.54	|75.94/69.40	|67.66/61.62	|70.18/68.98	|67.73/66.57|
+| [PV-RCNN++](tools/cfgs/waymo_models/pv_rcnn_plusplus.yaml) | 77.82/77.32|	69.07/68.62|	77.99/71.36|	69.92/63.74|	71.80/70.71|	69.31/68.26|
+| [PV-RCNN++ (ResNet)](tools/cfgs/waymo_models/pv_rcnn_plusplus_resnet.yaml) |77.61/77.14|	69.18/68.75|	79.42/73.31|	70.88/65.21|	72.50/71.39|	69.84/68.77|
+
+
+Here we also provide the performance of several models trained on the full training set (refer to the paper of [PV-RCNN++](https://arxiv.org/abs/2102.00463)):
+
+|    Performance@(train with 100\% Data)            | Vec_L1 | Vec_L2 | Ped_L1 | Ped_L2 | Cyc_L1 | Cyc_L2 |  
+|---------------------------------------------|----------:|:-------:|:-------:|:-------:|:-------:|:-------:|
+| [SECOND](tools/cfgs/waymo_models/second.yaml) | 72.27/71.69 | 63.85/63.33 | 68.70/58.18 | 60.72/51.31 | 60.62/59.28 | 58.34/57.05| 
+| [Part-A2-Anchor](tools/cfgs/waymo_models/PartA2.yaml) | 77.05/76.51 | 68.47/67.97 | 75.24/66.87 | 66.18/58.62 | 68.60/67.36 | 66.13/64.93 |
+| [PV-RCNN (CenterHead)](tools/cfgs/waymo_models/pv_rcnn_with_centerhead_rpn.yaml) | 78.00/77.50 | 69.43/68.98 | 79.21/73.03 | 70.42/64.72 | 71.46/70.27 | 68.95/67.79|
+| [PV-RCNN++](tools/cfgs/waymo_models/pv_rcnn_plusplus.yaml) | 79.10/78.63 | 70.34/69.91 | 80.62/74.62 | 71.86/66.30 | 73.49/72.38 | 70.70/69.62 |
+| [PV-RCNN++ (ResNet)](tools/cfgs/waymo_models/pv_rcnn_plusplus_resnet.yaml) |79.25/78.78 | 70.61/70.18 | 81.83/76.28 | 73.17/68.00 | 73.72/72.66 | 71.21/70.19|
+
+
+
 
 We could not provide the above pretrained models due to [Waymo Dataset License Agreement](https://waymo.com/open/terms/), 
 but you could easily achieve similar performance by training with the default configs.

docs/GETTING_STARTED.md

@@ -86,7 +86,7 @@ OpenPCDet
 ```shell script
 pip3 install --upgrade pip
 # tf 2.0.0
-pip3 install waymo-open-dataset-tf-2-0-0==1.2.0 --user
+pip3 install waymo-open-dataset-tf-2-5-0 --user
 ```
 
 * Extract point cloud data from tfrecord and generate data infos by running the following command (it takes several hours, 

pcdet/datasets/processor/point_feature_encoder.py

@@ -30,7 +30,7 @@ class PointFeatureEncoder(object):
             data_dict['points']
         )
         data_dict['use_lead_xyz'] = use_lead_xyz
-        
+       
         if self.point_encoding_config.get('filter_sweeps', False) and 'timestamp' in self.src_feature_list:
             max_sweeps = self.point_encoding_config.max_sweeps
             idx = self.src_feature_list.index('timestamp')

pcdet/models/backbones_3d/pfe/voxel_set_abstraction.py

+import math
+import numpy as np
 import torch
 import torch.nn as nn
 
@@ -40,6 +42,85 @@ def bilinear_interpolate_torch(im, x, y):
     return ans
 
 
+def sample_points_with_roi(rois, points, sample_radius_with_roi, num_max_points_of_part=200000):
+    """
+    Args:
+        rois: (M, 7 + C)
+        points: (N, 3)
+        sample_radius_with_roi:
+        num_max_points_of_part:
+
+    Returns:
+        sampled_points: (N_out, 3)
+    """
+    if points.shape[0] < num_max_points_of_part:
+        distance = (points[:, None, :] - rois[None, :, 0:3]).norm(dim=-1)
+        min_dis, min_dis_roi_idx = distance.min(dim=-1)
+        roi_max_dim = (rois[min_dis_roi_idx, 3:6] / 2).norm(dim=-1)
+        point_mask = min_dis < roi_max_dim + sample_radius_with_roi
+    else:
+        start_idx = 0
+        point_mask_list = []
+        while start_idx < points.shape[0]:
+            distance = (points[start_idx:start_idx + num_max_points_of_part, None, :] - rois[None, :, 0:3]).norm(dim=-1)
+            min_dis, min_dis_roi_idx = distance.min(dim=-1)
+            roi_max_dim = (rois[min_dis_roi_idx, 3:6] / 2).norm(dim=-1)
+            cur_point_mask = min_dis < roi_max_dim + sample_radius_with_roi
+            point_mask_list.append(cur_point_mask)
+            start_idx += num_max_points_of_part
+        point_mask = torch.cat(point_mask_list, dim=0)
+
+    sampled_points = points[:1] if point_mask.sum() == 0 else points[point_mask, :]
+
+    return sampled_points, point_mask
+
+
+def sector_fps(points, num_sampled_points, num_sectors):
+    """
+    Args:
+        points: (N, 3)
+        num_sampled_points: int
+        num_sectors: int
+
+    Returns:
+        sampled_points: (N_out, 3)
+    """
+    sector_size = np.pi * 2 / num_sectors
+    point_angles = torch.atan2(points[:, 1], points[:, 0]) + np.pi
+    sector_idx = (point_angles / sector_size).floor().clamp(min=0, max=num_sectors)
+    xyz_points_list = []
+    xyz_batch_cnt = []
+    num_sampled_points_list = []
+    for k in range(num_sectors):
+        mask = (sector_idx == k)
+        cur_num_points = mask.sum().item()
+        if cur_num_points > 0:
+            xyz_points_list.append(points[mask])
+            xyz_batch_cnt.append(cur_num_points)
+            ratio = cur_num_points / points.shape[0]
+            num_sampled_points_list.append(
+                min(cur_num_points, math.ceil(ratio * num_sampled_points))
+            )
+
+    if len(xyz_batch_cnt) == 0:
+        xyz_points_list.append(points)
+        xyz_batch_cnt.append(len(points))
+        num_sampled_points_list.append(num_sampled_points)
+        print(f'Warning: empty sector points detected in SectorFPS: points.shape={points.shape}')
+
+    xyz = torch.cat(xyz_points_list, dim=0)
+    xyz_batch_cnt = torch.tensor(xyz_batch_cnt, device=points.device).int()
+    sampled_points_batch_cnt = torch.tensor(num_sampled_points_list, device=points.device).int()
+
+    sampled_pt_idxs = pointnet2_stack_utils.stack_farthest_point_sample(
+        xyz.contiguous(), xyz_batch_cnt, sampled_points_batch_cnt
+    ).long()
+
+    sampled_points = xyz[sampled_pt_idxs]
+
+    return sampled_points
+
+
 class VoxelSetAbstraction(nn.Module):
     def __init__(self, model_cfg, voxel_size, point_cloud_range, num_bev_features=None,
                  num_rawpoint_features=None, **kwargs):
@@ -58,38 +139,31 @@ class VoxelSetAbstraction(nn.Module):
             if src_name in ['bev', 'raw_points']:
                 continue
             self.downsample_times_map[src_name] = SA_cfg[src_name].DOWNSAMPLE_FACTOR
-            mlps = SA_cfg[src_name].MLPS
-            for k in range(len(mlps)):
-                mlps[k] = [mlps[k][0]] + mlps[k]
-            cur_layer = pointnet2_stack_modules.StackSAModuleMSG(
-                radii=SA_cfg[src_name].POOL_RADIUS,
-                nsamples=SA_cfg[src_name].NSAMPLE,
-                mlps=mlps,
-                use_xyz=True,
-                pool_method='max_pool',
+
+            if SA_cfg[src_name].get('INPUT_CHANNELS', None) is None:
+                input_channels = SA_cfg[src_name].MLPS[0][0] \
+                    if isinstance(SA_cfg[src_name].MLPS[0], list) else SA_cfg[src_name].MLPS[0]
+            else:
+                input_channels = SA_cfg[src_name]['INPUT_CHANNELS']
+
+            cur_layer, cur_num_c_out = pointnet2_stack_modules.build_local_aggregation_module(
+                input_channels=input_channels, config=SA_cfg[src_name]
             )
             self.SA_layers.append(cur_layer)
             self.SA_layer_names.append(src_name)
 
-            c_in += sum([x[-1] for x in mlps])
+            c_in += cur_num_c_out
 
         if 'bev' in self.model_cfg.FEATURES_SOURCE:
             c_bev = num_bev_features
             c_in += c_bev
 
         if 'raw_points' in self.model_cfg.FEATURES_SOURCE:
-            mlps = SA_cfg['raw_points'].MLPS
-            for k in range(len(mlps)):
-                mlps[k] = [num_rawpoint_features - 3] + mlps[k]
-
-            self.SA_rawpoints = pointnet2_stack_modules.StackSAModuleMSG(
-                radii=SA_cfg['raw_points'].POOL_RADIUS,
-                nsamples=SA_cfg['raw_points'].NSAMPLE,
-                mlps=mlps,
-                use_xyz=True,
-                pool_method='max_pool'
+            self.SA_rawpoints, cur_num_c_out = pointnet2_stack_modules.build_local_aggregation_module(
+                input_channels=num_rawpoint_features - 3, config=SA_cfg['raw_points']
             )
-            c_in += sum([x[-1] for x in mlps])
+
+            c_in += cur_num_c_out
 
         self.vsa_point_feature_fusion = nn.Sequential(
             nn.Linear(c_in, self.model_cfg.NUM_OUTPUT_FEATURES, bias=False),
@@ -100,23 +174,64 @@ class VoxelSetAbstraction(nn.Module):
         self.num_point_features_before_fusion = c_in
 
     def interpolate_from_bev_features(self, keypoints, bev_features, batch_size, bev_stride):
-        x_idxs = (keypoints[:, :, 0] - self.point_cloud_range[0]) / self.voxel_size[0]
-        y_idxs = (keypoints[:, :, 1] - self.point_cloud_range[1]) / self.voxel_size[1]
+        """
+        Args:
+            keypoints: (N1 + N2 + ..., 4)
+            bev_features: (B, C, H, W)
+            batch_size:
+            bev_stride:
+
+        Returns:
+            point_bev_features: (N1 + N2 + ..., C)
+        """
+        x_idxs = (keypoints[:, 1] - self.point_cloud_range[0]) / self.voxel_size[0]
+        y_idxs = (keypoints[:, 2] - self.point_cloud_range[1]) / self.voxel_size[1]
+
         x_idxs = x_idxs / bev_stride
         y_idxs = y_idxs / bev_stride
 
         point_bev_features_list = []
         for k in range(batch_size):
-            cur_x_idxs = x_idxs[k]
-            cur_y_idxs = y_idxs[k]
+            bs_mask = (keypoints[:, 0] == k)
+
+            cur_x_idxs = x_idxs[bs_mask]
+            cur_y_idxs = y_idxs[bs_mask]
             cur_bev_features = bev_features[k].permute(1, 2, 0)  # (H, W, C)
             point_bev_features = bilinear_interpolate_torch(cur_bev_features, cur_x_idxs, cur_y_idxs)
-            point_bev_features_list.append(point_bev_features.unsqueeze(dim=0))
+            point_bev_features_list.append(point_bev_features)
 
-        point_bev_features = torch.cat(point_bev_features_list, dim=0)  # (B, N, C0)
+        point_bev_features = torch.cat(point_bev_features_list, dim=0)  # (N1 + N2 + ..., C)
         return point_bev_features
 
+    def sectorized_proposal_centric_sampling(self, roi_boxes, points):
+        """
+        Args:
+            roi_boxes: (M, 7 + C)
+            points: (N, 3)
+
+        Returns:
+            sampled_points: (N_out, 3)
+        """
+
+        sampled_points, _ = sample_points_with_roi(
+            rois=roi_boxes, points=points,
+            sample_radius_with_roi=self.model_cfg.SPC_SAMPLING.SAMPLE_RADIUS_WITH_ROI,
+            num_max_points_of_part=self.model_cfg.SPC_SAMPLING.get('NUM_POINTS_OF_EACH_SAMPLE_PART', 200000)
+        )
+        sampled_points = sector_fps(
+            points=sampled_points, num_sampled_points=self.model_cfg.NUM_KEYPOINTS,
+            num_sectors=self.model_cfg.SPC_SAMPLING.NUM_SECTORS
+        )
+        return sampled_points
+
     def get_sampled_points(self, batch_dict):
+        """
+        Args:
+            batch_dict:
+
+        Returns:
+            keypoints: (N1 + N2 + ..., 4), where 4 indicates [bs_idx, x, y, z]
+        """
         batch_size = batch_dict['batch_size']
         if self.model_cfg.POINT_SOURCE == 'raw_points':
             src_points = batch_dict['points'][:, 1:4]
@@ -136,7 +251,7 @@ class VoxelSetAbstraction(nn.Module):
             bs_mask = (batch_indices == bs_idx)
             sampled_points = src_points[bs_mask].unsqueeze(dim=0)  # (1, N, 3)
             if self.model_cfg.SAMPLE_METHOD == 'FPS':
-                cur_pt_idxs = pointnet2_stack_utils.furthest_point_sample(
+                cur_pt_idxs = pointnet2_stack_utils.farthest_point_sample(
                     sampled_points[:, :, 0:3].contiguous(), self.model_cfg.NUM_KEYPOINTS
                 ).long()
 
@@ -147,16 +262,75 @@ class VoxelSetAbstraction(nn.Module):
 
                 keypoints = sampled_points[0][cur_pt_idxs[0]].unsqueeze(dim=0)
 
-            elif self.model_cfg.SAMPLE_METHOD == 'FastFPS':
-                raise NotImplementedError
+            elif self.model_cfg.SAMPLE_METHOD == 'SPC':
+                cur_keypoints = self.sectorized_proposal_centric_sampling(
+                    roi_boxes=batch_dict['rois'][bs_idx], points=sampled_points[0]
+                )
+                bs_idxs = cur_keypoints.new_ones(cur_keypoints.shape[0]) * bs_idx
+                keypoints = torch.cat((bs_idxs[:, None], cur_keypoints), dim=1)
             else:
                 raise NotImplementedError
 
             keypoints_list.append(keypoints)
 
-        keypoints = torch.cat(keypoints_list, dim=0)  # (B, M, 3)
+        keypoints = torch.cat(keypoints_list, dim=0)  # (B, M, 3) or (N1 + N2 + ..., 4)
+        if len(keypoints.shape) == 3:
+            batch_idx = torch.arange(batch_size, device=keypoints.device).view(-1, 1).repeat(1, keypoints.shape[1]).view(-1, 1)
+            keypoints = torch.cat((batch_idx.float(), keypoints.view(-1, 3)), dim=1)
+
         return keypoints
 
+    @staticmethod
+    def aggregate_keypoint_features_from_one_source(
+            batch_size, aggregate_func, xyz, xyz_features, xyz_bs_idxs, new_xyz, new_xyz_batch_cnt,
+            filter_neighbors_with_roi=False, radius_of_neighbor=None, num_max_points_of_part=200000, rois=None
+    ):
+        """
+
+        Args:
+            aggregate_func:
+            xyz: (N, 3)
+            xyz_features: (N, C)
+            xyz_bs_idxs: (N)
+            new_xyz: (M, 3)
+            new_xyz_batch_cnt: (batch_size), [N1, N2, ...]
+
+            filter_neighbors_with_roi: True/False
+            radius_of_neighbor: float
+            num_max_points_of_part: int
+            rois: (batch_size, num_rois, 7 + C)
+        Returns:
+
+        """
+        xyz_batch_cnt = xyz.new_zeros(batch_size).int()
+        if filter_neighbors_with_roi:
+            point_features = torch.cat((xyz, xyz_features), dim=-1) if xyz_features is not None else xyz
+            point_features_list = []
+            for bs_idx in range(batch_size):
+                bs_mask = (xyz_bs_idxs == bs_idx)
+                _, valid_mask = sample_points_with_roi(
+                    rois=rois[bs_idx], points=xyz[bs_mask],
+                    sample_radius_with_roi=radius_of_neighbor, num_max_points_of_part=num_max_points_of_part,
+                )
+                point_features_list.append(point_features[bs_mask][valid_mask])
+                xyz_batch_cnt[bs_idx] = valid_mask.sum()
+
+            valid_point_features = torch.cat(point_features_list, dim=0)
+            xyz = valid_point_features[:, 0:3]
+            xyz_features = valid_point_features[:, 3:] if xyz_features is not None else None
+        else:
+            for bs_idx in range(batch_size):
+                xyz_batch_cnt[bs_idx] = (xyz_bs_idxs == bs_idx).sum()
+
+        pooled_points, pooled_features = aggregate_func(
+            xyz=xyz.contiguous(),
+            xyz_batch_cnt=xyz_batch_cnt,
+            new_xyz=new_xyz,
+            new_xyz_batch_cnt=new_xyz_batch_cnt,
+            features=xyz_features.contiguous(),
+        )
+        return pooled_features
+
     def forward(self, batch_dict):
         """
         Args:
@@ -185,56 +359,53 @@ class VoxelSetAbstraction(nn.Module):
             )
             point_features_list.append(point_bev_features)
 
-        batch_size, num_keypoints, _ = keypoints.shape
-        new_xyz = keypoints.view(-1, 3)
-        new_xyz_batch_cnt = new_xyz.new_zeros(batch_size).int().fill_(num_keypoints)
+        batch_size = batch_dict['batch_size']
+
+        new_xyz = keypoints[:, 1:4].contiguous()
+        new_xyz_batch_cnt = new_xyz.new_zeros(batch_size).int()
+        for k in range(batch_size):
+            new_xyz_batch_cnt[k] = (keypoints[:, 0] == k).sum()
 
         if 'raw_points' in self.model_cfg.FEATURES_SOURCE:
             raw_points = batch_dict['points']
-            xyz = raw_points[:, 1:4]
-            xyz_batch_cnt = xyz.new_zeros(batch_size).int()
-            for bs_idx in range(batch_size):
-                xyz_batch_cnt[bs_idx] = (raw_points[:, 0] == bs_idx).sum()
-            point_features = raw_points[:, 4:].contiguous() if raw_points.shape[1] > 4 else None
-
-            pooled_points, pooled_features = self.SA_rawpoints(
-                xyz=xyz.contiguous(),
-                xyz_batch_cnt=xyz_batch_cnt,
-                new_xyz=new_xyz,
-                new_xyz_batch_cnt=new_xyz_batch_cnt,
-                features=point_features,
+
+            pooled_features = self.aggregate_keypoint_features_from_one_source(
+                batch_size=batch_size, aggregate_func=self.SA_rawpoints,
+                xyz=raw_points[:, 1:4],
+                xyz_features=raw_points[:, 4:].contiguous() if raw_points.shape[1] > 4 else None,
+                xyz_bs_idxs=raw_points[:, 0],
+                new_xyz=new_xyz, new_xyz_batch_cnt=new_xyz_batch_cnt,
+                filter_neighbors_with_roi=self.model_cfg.SA_LAYER['raw_points'].get('FILTER_NEIGHBOR_WITH_ROI', False),
+                radius_of_neighbor=self.model_cfg.SA_LAYER['raw_points'].get('RADIUS_OF_NEIGHBOR_WITH_ROI', None),
+                rois=batch_dict.get('rois', None)
             )
-            point_features_list.append(pooled_features.view(batch_size, num_keypoints, -1))
+            point_features_list.append(pooled_features)
 
         for k, src_name in enumerate(self.SA_layer_names):
             cur_coords = batch_dict['multi_scale_3d_features'][src_name].indices
+            cur_features = batch_dict['multi_scale_3d_features'][src_name].features.contiguous()
+
             xyz = common_utils.get_voxel_centers(
-                cur_coords[:, 1:4],
-                downsample_times=self.downsample_times_map[src_name],
-                voxel_size=self.voxel_size,
-                point_cloud_range=self.point_cloud_range
+                cur_coords[:, 1:4], downsample_times=self.downsample_times_map[src_name],
+                voxel_size=self.voxel_size, point_cloud_range=self.point_cloud_range
             )
-            xyz_batch_cnt = xyz.new_zeros(batch_size).int()
-            for bs_idx in range(batch_size):
-                xyz_batch_cnt[bs_idx] = (cur_coords[:, 0] == bs_idx).sum()
-
-            pooled_points, pooled_features = self.SA_layers[k](
-                xyz=xyz.contiguous(),
-                xyz_batch_cnt=xyz_batch_cnt,
-                new_xyz=new_xyz,
-                new_xyz_batch_cnt=new_xyz_batch_cnt,
-                features=batch_dict['multi_scale_3d_features'][src_name].features.contiguous(),
+
+            pooled_features = self.aggregate_keypoint_features_from_one_source(
+                batch_size=batch_size, aggregate_func=self.SA_layers[k],
+                xyz=xyz.contiguous(), xyz_features=cur_features, xyz_bs_idxs=cur_coords[:, 0],
+                new_xyz=new_xyz, new_xyz_batch_cnt=new_xyz_batch_cnt,
+                filter_neighbors_with_roi=self.model_cfg.SA_LAYER[src_name].get('FILTER_NEIGHBOR_WITH_ROI', False),
+                radius_of_neighbor=self.model_cfg.SA_LAYER[src_name].get('RADIUS_OF_NEIGHBOR_WITH_ROI', None),
+                rois=batch_dict.get('rois', None)
             )
-            point_features_list.append(pooled_features.view(batch_size, num_keypoints, -1))
 
-        point_features = torch.cat(point_features_list, dim=2)
+            point_features_list.append(pooled_features)
 
-        batch_idx = torch.arange(batch_size, device=keypoints.device).view(-1, 1).repeat(1, keypoints.shape[1]).view(-1)
-        point_coords = torch.cat((batch_idx.view(-1, 1).float(), keypoints.view(-1, 3)), dim=1)
+        point_features = torch.cat(point_features_list, dim=-1)
 
         batch_dict['point_features_before_fusion'] = point_features.view(-1, point_features.shape[-1])
         point_features = self.vsa_point_feature_fusion(point_features.view(-1, point_features.shape[-1]))
 
         batch_dict['point_features'] = point_features  # (BxN, C)
-        batch_dict['point_coords'] = point_coords  # (BxN, 4)
+        batch_dict['point_coords'] = keypoints  # (BxN, 4)
         return batch_dict

pcdet/models/backbones_3d/pointnet2_backbone.py

@@ -174,7 +174,7 @@ class PointNet2Backbone(nn.Module):
                 else:
                     last_num_points = self.num_points_each_layer[i - 1]
                     cur_xyz = l_xyz[-1][k * last_num_points: (k + 1) * last_num_points]
-                cur_pt_idxs = pointnet2_utils_stack.furthest_point_sample(
+                cur_pt_idxs = pointnet2_utils_stack.farthest_point_sample(
                     cur_xyz[None, :, :].contiguous(), self.num_points_each_layer[i]
                 ).long()[0]
                 if cur_xyz.shape[0] < self.num_points_each_layer[i]:

pcdet/models/detectors/__init__.py

@@ -8,6 +8,7 @@ from .second_net_iou import SECONDNetIoU
 from .caddn import CaDDN
 from .voxel_rcnn import VoxelRCNN
 from .centerpoint import CenterPoint
+from .pv_rcnn_plusplus import PVRCNNPlusPlus
 
 __all__ = {
     'Detector3DTemplate': Detector3DTemplate,
@@ -19,7 +20,8 @@ __all__ = {
     'SECONDNetIoU': SECONDNetIoU,
     'CaDDN': CaDDN,
     'VoxelRCNN': VoxelRCNN,
-    'CenterPoint': CenterPoint
+    'CenterPoint': CenterPoint,
+    'PVRCNNPlusPlus': PVRCNNPlusPlus
 }
 
 

pcdet/models/detectors/pv_rcnn_plusplus.py

+from .detector3d_template import Detector3DTemplate
+
+
+class PVRCNNPlusPlus(Detector3DTemplate):
+    def __init__(self, model_cfg, num_class, dataset):
+        super().__init__(model_cfg=model_cfg, num_class=num_class, dataset=dataset)
+        self.module_list = self.build_networks()
+
+    def forward(self, batch_dict):
+        batch_dict = self.vfe(batch_dict)
+        batch_dict = self.backbone_3d(batch_dict)
+        batch_dict = self.map_to_bev_module(batch_dict)
+        batch_dict = self.backbone_2d(batch_dict)
+        batch_dict = self.dense_head(batch_dict)
+
+        batch_dict = self.roi_head.proposal_layer(
+            batch_dict, nms_config=self.roi_head.model_cfg.NMS_CONFIG['TRAIN' if self.training else 'TEST']
+        )
+        if self.training:
+            targets_dict = self.roi_head.assign_targets(batch_dict)
+            batch_dict['rois'] = targets_dict['rois']
+            batch_dict['roi_labels'] = targets_dict['roi_labels']
+            batch_dict['roi_targets_dict'] = targets_dict
+            num_rois_per_scene = targets_dict['rois'].shape[1]
+            if 'roi_valid_num' in batch_dict:
+                batch_dict['roi_valid_num'] = [num_rois_per_scene for _ in range(batch_dict['batch_size'])]
+
+        batch_dict = self.pfe(batch_dict)
+        batch_dict = self.point_head(batch_dict)
+        batch_dict = self.roi_head(batch_dict)
+
+        if self.training:
+            loss, tb_dict, disp_dict = self.get_training_loss()
+
+            ret_dict = {
+                'loss': loss
+            }
+            return ret_dict, tb_dict, disp_dict
+        else:
+            pred_dicts, recall_dicts = self.post_processing(batch_dict)
+            return pred_dicts, recall_dicts
+
+    def get_training_loss(self):
+        disp_dict = {}
+        loss_rpn, tb_dict = self.dense_head.get_loss()
+        if self.point_head is not None:
+            loss_point, tb_dict = self.point_head.get_loss(tb_dict)
+        else:
+            loss_point = 0
+        loss_rcnn, tb_dict = self.roi_head.get_loss(tb_dict)
+
+        loss = loss_rpn + loss_point + loss_rcnn
+        return loss, tb_dict, disp_dict

pcdet/models/roi_heads/pvrcnn_head.py

@@ -10,21 +10,12 @@ class PVRCNNHead(RoIHeadTemplate):
         super().__init__(num_class=num_class, model_cfg=model_cfg)
         self.model_cfg = model_cfg
 
-        mlps = self.model_cfg.ROI_GRID_POOL.MLPS
-        for k in range(len(mlps)):
-            mlps[k] = [input_channels] + mlps[k]
-
-        self.roi_grid_pool_layer = pointnet2_stack_modules.StackSAModuleMSG(
-            radii=self.model_cfg.ROI_GRID_POOL.POOL_RADIUS,
-            nsamples=self.model_cfg.ROI_GRID_POOL.NSAMPLE,
-            mlps=mlps,
-            use_xyz=True,
-            pool_method=self.model_cfg.ROI_GRID_POOL.POOL_METHOD,
+        self.roi_grid_pool_layer, num_c_out = pointnet2_stack_modules.build_local_aggregation_module(
+            input_channels=input_channels, config=self.model_cfg.ROI_GRID_POOL
         )
 
         GRID_SIZE = self.model_cfg.ROI_GRID_POOL.GRID_SIZE
-        c_out = sum([x[-1] for x in mlps])
-        pre_channel = GRID_SIZE * GRID_SIZE * GRID_SIZE * c_out
+        pre_channel = GRID_SIZE * GRID_SIZE * GRID_SIZE * num_c_out
 
         shared_fc_list = []
         for k in range(0, self.model_cfg.SHARED_FC.__len__()):
@@ -150,9 +141,11 @@ class PVRCNNHead(RoIHeadTemplate):
             batch_dict, nms_config=self.model_cfg.NMS_CONFIG['TRAIN' if self.training else 'TEST']
         )
         if self.training:
-            targets_dict = self.assign_targets(batch_dict)
-            batch_dict['rois'] = targets_dict['rois']
-            batch_dict['roi_labels'] = targets_dict['roi_labels']
+            targets_dict = batch_dict.get('roi_targets_dict', None)
+            if targets_dict is None:
+                targets_dict = self.assign_targets(batch_dict)
+                batch_dict['rois'] = targets_dict['rois']
+                batch_dict['roi_labels'] = targets_dict['roi_labels']
 
         # RoI aware pooling
         pooled_features = self.roi_grid_pool(batch_dict)  # (BxN, 6x6x6, C)

pcdet/ops/pointnet2/pointnet2_batch/pointnet2_modules.py

@@ -31,7 +31,7 @@ class _PointnetSAModuleBase(nn.Module):
         if new_xyz is None:
             new_xyz = pointnet2_utils.gather_operation(
                 xyz_flipped,
-                pointnet2_utils.furthest_point_sample(xyz, self.npoint)
+                pointnet2_utils.farthest_point_sample(xyz, self.npoint)
             ).transpose(1, 2).contiguous() if self.npoint is not None else None
 
         for i in range(len(self.groupers)):

pcdet/ops/pointnet2/pointnet2_batch/pointnet2_utils.py

@@ -7,11 +7,11 @@ from torch.autograd import Function, Variable
 from . import pointnet2_batch_cuda as pointnet2
 
 
-class FurthestPointSampling(Function):
+class FarthestPointSampling(Function):
     @staticmethod
     def forward(ctx, xyz: torch.Tensor, npoint: int) -> torch.Tensor:
         """
-        Uses iterative furthest point sampling to select a set of npoint features that have the largest
+        Uses iterative farthest point sampling to select a set of npoint features that have the largest
         minimum distance
         :param ctx:
         :param xyz: (B, N, 3) where N > npoint
@@ -25,7 +25,7 @@ class FurthestPointSampling(Function):
         output = torch.cuda.IntTensor(B, npoint)
         temp = torch.cuda.FloatTensor(B, N).fill_(1e10)
 
-        pointnet2.furthest_point_sampling_wrapper(B, N, npoint, xyz, temp, output)
+        pointnet2.farthest_point_sampling_wrapper(B, N, npoint, xyz, temp, output)
         return output
 
     @staticmethod
@@ -33,7 +33,7 @@ class FurthestPointSampling(Function):
         return None, None
 
 
-furthest_point_sample = FurthestPointSampling.apply
+farthest_point_sample = furthest_point_sample = FarthestPointSampling.apply
 
 
 class GatherOperation(Function):

pcdet/ops/pointnet2/pointnet2_batch/src/pointnet2_api.cpp

@@ -16,7 +16,7 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
     m.def("gather_points_wrapper", &gather_points_wrapper_fast, "gather_points_wrapper_fast");
     m.def("gather_points_grad_wrapper", &gather_points_grad_wrapper_fast, "gather_points_grad_wrapper_fast");
 
-    m.def("furthest_point_sampling_wrapper", &furthest_point_sampling_wrapper, "furthest_point_sampling_wrapper");
+    m.def("farthest_point_sampling_wrapper", &farthest_point_sampling_wrapper, "farthest_point_sampling_wrapper");
     
     m.def("three_nn_wrapper", &three_nn_wrapper_fast, "three_nn_wrapper_fast");
     m.def("three_interpolate_wrapper", &three_interpolate_wrapper_fast, "three_interpolate_wrapper_fast");

pcdet/ops/pointnet2/pointnet2_batch/src/sampling.cpp

@@ -38,13 +38,13 @@ int gather_points_grad_wrapper_fast(int b, int c, int n, int npoints,
 }
 
 
-int furthest_point_sampling_wrapper(int b, int n, int m, 
+int farthest_point_sampling_wrapper(int b, int n, int m,
     at::Tensor points_tensor, at::Tensor temp_tensor, at::Tensor idx_tensor) {
 
     const float *points = points_tensor.data<float>();
     float *temp = temp_tensor.data<float>();
     int *idx = idx_tensor.data<int>();
 
-    furthest_point_sampling_kernel_launcher(b, n, m, points, temp, idx);
+    farthest_point_sampling_kernel_launcher(b, n, m, points, temp, idx);
     return 1;
 }

pcdet/ops/pointnet2/pointnet2_batch/src/sampling_gpu.cu

@@ -98,7 +98,7 @@ __device__ void __update(float *__restrict__ dists, int *__restrict__ dists_i, i
 }
 
 template <unsigned int block_size>
-__global__ void furthest_point_sampling_kernel(int b, int n, int m, 
+__global__ void farthest_point_sampling_kernel(int b, int n, int m,
     const float *__restrict__ dataset, float *__restrict__ temp, int *__restrict__ idxs) {
     // dataset: (B, N, 3)
     // tmp: (B, N)
@@ -215,7 +215,7 @@ __global__ void furthest_point_sampling_kernel(int b, int n, int m,
     }
 }
 
-void furthest_point_sampling_kernel_launcher(int b, int n, int m, 
+void farthest_point_sampling_kernel_launcher(int b, int n, int m,
     const float *dataset, float *temp, int *idxs) {
     // dataset: (B, N, 3)
     // tmp: (B, N)
@@ -227,29 +227,29 @@ void furthest_point_sampling_kernel_launcher(int b, int n, int m,
 
     switch (n_threads) {
         case 1024:
-        furthest_point_sampling_kernel<1024><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        farthest_point_sampling_kernel<1024><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
         case 512:
-        furthest_point_sampling_kernel<512><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        farthest_point_sampling_kernel<512><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
         case 256:
-        furthest_point_sampling_kernel<256><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        farthest_point_sampling_kernel<256><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
         case 128:
-        furthest_point_sampling_kernel<128><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        farthest_point_sampling_kernel<128><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
         case 64:
-        furthest_point_sampling_kernel<64><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        farthest_point_sampling_kernel<64><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
         case 32:
-        furthest_point_sampling_kernel<32><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        farthest_point_sampling_kernel<32><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
         case 16:
-        furthest_point_sampling_kernel<16><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        farthest_point_sampling_kernel<16><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
         case 8:
-        furthest_point_sampling_kernel<8><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        farthest_point_sampling_kernel<8><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
         case 4:
-        furthest_point_sampling_kernel<4><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        farthest_point_sampling_kernel<4><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
         case 2:
-        furthest_point_sampling_kernel<2><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        farthest_point_sampling_kernel<2><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
         case 1:
-        furthest_point_sampling_kernel<1><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        farthest_point_sampling_kernel<1><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
         default:
-        furthest_point_sampling_kernel<512><<<b, n_threads>>>(b, n, m, dataset, temp, idxs);
+        farthest_point_sampling_kernel<512><<<b, n_threads>>>(b, n, m, dataset, temp, idxs);
     }
 
     err = cudaGetLastError();

pcdet/ops/pointnet2/pointnet2_batch/src/sampling_gpu.h

@@ -20,10 +20,10 @@ void gather_points_grad_kernel_launcher_fast(int b, int c, int n, int npoints,
     const float *grad_out, const int *idx, float *grad_points);
 
 
-int furthest_point_sampling_wrapper(int b, int n, int m, 
+int farthest_point_sampling_wrapper(int b, int n, int m,
     at::Tensor points_tensor, at::Tensor temp_tensor, at::Tensor idx_tensor);
 
-void furthest_point_sampling_kernel_launcher(int b, int n, int m, 
+void farthest_point_sampling_kernel_launcher(int b, int n, int m,
     const float *dataset, float *temp, int *idxs);
 
 #endif

pcdet/ops/pointnet2/pointnet2_stack/pointnet2_modules.py

@@ -7,6 +7,26 @@ import torch.nn.functional as F
 from . import pointnet2_utils
 
 
+def build_local_aggregation_module(input_channels, config):
+    local_aggregation_name = config.get('NAME', 'StackSAModuleMSG')
+
+    if local_aggregation_name == 'StackSAModuleMSG':
+        mlps = config.MLPS
+        for k in range(len(mlps)):
+            mlps[k] = [input_channels] + mlps[k]
+        cur_layer = StackSAModuleMSG(
+            radii=config.POOL_RADIUS, nsamples=config.NSAMPLE, mlps=mlps, use_xyz=True, pool_method='max_pool',
+        )
+        num_c_out = sum([x[-1] for x in mlps])
+    elif local_aggregation_name == 'VectorPoolAggregationModuleMSG':
+        cur_layer = VectorPoolAggregationModuleMSG(input_channels=input_channels, config=config)
+        num_c_out = config.MSG_POST_MLPS[-1]
+    else:
+        raise NotImplementedError
+
+    return cur_layer, num_c_out
+
+
 class StackSAModuleMSG(nn.Module):
 
     def __init__(self, *, radii: List[float], nsamples: List[int], mlps: List[List[int]],
@@ -135,3 +155,316 @@ class StackPointnetFPModule(nn.Module):
 
         new_features = new_features.squeeze(dim=0).squeeze(dim=-1).permute(1, 0)  # (N1 + N2 ..., C)
         return new_features
+
+
+class VectorPoolLocalInterpolateModule(nn.Module):
+    def __init__(self, mlp, num_voxels, max_neighbour_distance, nsample, neighbor_type, use_xyz=True,
+                 neighbour_distance_multiplier=1.0, xyz_encoding_type='concat'):
+        """
+        Args:
+            mlp:
+            num_voxels:
+            max_neighbour_distance:
+            neighbor_type: 1: ball, others: cube
+            nsample: find all (-1), find limited number(>0)
+            use_xyz:
+            neighbour_distance_multiplier:
+            xyz_encoding_type:
+        """
+        super().__init__()
+        self.num_voxels = num_voxels  # [num_grid_x, num_grid_y, num_grid_z]: number of grids in each local area centered at new_xyz
+        self.num_total_grids = self.num_voxels[0] * self.num_voxels[1] * self.num_voxels[2]
+        self.max_neighbour_distance = max_neighbour_distance
+        self.neighbor_distance_multiplier = neighbour_distance_multiplier
+        self.nsample = nsample
+        self.neighbor_type = neighbor_type
+        self.use_xyz = use_xyz
+        self.xyz_encoding_type = xyz_encoding_type
+
+        if mlp is not None:
+            if self.use_xyz:
+                mlp[0] += 9 if self.xyz_encoding_type == 'concat' else 0
+            shared_mlps = []
+            for k in range(len(mlp) - 1):
+                shared_mlps.extend([
+                    nn.Conv2d(mlp[k], mlp[k + 1], kernel_size=1, bias=False),
+                    nn.BatchNorm2d(mlp[k + 1]),
+                    nn.ReLU()
+                ])
+            self.mlp = nn.Sequential(*shared_mlps)
+        else:
+            self.mlp = None
+
+        self.num_avg_length_of_neighbor_idxs = 1000
+
+    def forward(self, support_xyz, support_features, xyz_batch_cnt, new_xyz, new_xyz_grid_centers, new_xyz_batch_cnt):
+        """
+        Args:
+            support_xyz: (N1 + N2 ..., 3) xyz coordinates of the features
+            support_features: (N1 + N2 ..., C) point-wise features
+            xyz_batch_cnt: (batch_size), [N1, N2, ...]
+            new_xyz: (M1 + M2 ..., 3) centers of the ball query
+            new_xyz_grid_centers: (M1 + M2 ..., num_total_grids, 3) grids centers of each grid
+            new_xyz_batch_cnt: (batch_size), [M1, M2, ...]
+        Returns:
+            new_features: (N1 + N2 ..., C_out)
+        """
+        with torch.no_grad():
+            dist, idx, num_avg_length_of_neighbor_idxs = pointnet2_utils.three_nn_for_vector_pool_by_two_step(
+                support_xyz, xyz_batch_cnt, new_xyz, new_xyz_grid_centers, new_xyz_batch_cnt,
+                self.max_neighbour_distance, self.nsample, self.neighbor_type,
+                self.num_avg_length_of_neighbor_idxs, self.num_total_grids, self.neighbor_distance_multiplier
+            )
+        self.num_avg_length_of_neighbor_idxs = max(self.num_avg_length_of_neighbor_idxs, num_avg_length_of_neighbor_idxs.item())
+
+        dist_recip = 1.0 / (dist + 1e-8)
+        norm = torch.sum(dist_recip, dim=-1, keepdim=True)
+        weight = dist_recip / torch.clamp_min(norm, min=1e-8)
+
+        empty_mask = (idx.view(-1, 3)[:, 0] == -1)
+        idx.view(-1, 3)[empty_mask] = 0
+
+        interpolated_feats = pointnet2_utils.three_interpolate(support_features, idx.view(-1, 3), weight.view(-1, 3))
+        interpolated_feats = interpolated_feats.view(idx.shape[0], idx.shape[1], -1)  # (M1 + M2 ..., num_total_grids, C)
+        if self.use_xyz:
+            near_known_xyz = support_xyz[idx.view(-1, 3).long()].view(-1, 3, 3)  # ( (M1 + M2 ...)*num_total_grids, 3)
+            local_xyz = (new_xyz_grid_centers.view(-1, 1, 3) - near_known_xyz).view(-1, idx.shape[1], 9)
+            if self.xyz_encoding_type == 'concat':
+                interpolated_feats = torch.cat((interpolated_feats, local_xyz), dim=-1)  # ( M1 + M2 ..., num_total_grids, 9+C)
+            else:
+                raise NotImplementedError
+
+        new_features = interpolated_feats.view(-1, interpolated_feats.shape[-1])  # ((M1 + M2 ...) * num_total_grids, C)
+        new_features[empty_mask, :] = 0
+        if self.mlp is not None:
+            new_features = new_features.permute(1, 0)[None, :, :, None]  # (1, C, N1 + N2 ..., 1)
+            new_features = self.mlp(new_features)
+
+            new_features = new_features.squeeze(dim=0).squeeze(dim=-1).permute(1, 0)  # (N1 + N2 ..., C)
+        return new_features
+
+
+class VectorPoolAggregationModule(nn.Module):
+    def __init__(
+            self, input_channels, num_local_voxel=(3, 3, 3), local_aggregation_type='local_interpolation',
+            num_reduced_channels=30, num_channels_of_local_aggregation=32, post_mlps=(128,),
+            max_neighbor_distance=None, neighbor_nsample=-1, neighbor_type=0, neighbor_distance_multiplier=2.0):
+        super().__init__()
+        self.num_local_voxel = num_local_voxel
+        self.total_voxels = self.num_local_voxel[0] * self.num_local_voxel[1] * self.num_local_voxel[2]
+        self.local_aggregation_type = local_aggregation_type
+        assert self.local_aggregation_type in ['local_interpolation', 'voxel_avg_pool', 'voxel_random_choice']
+        self.input_channels = input_channels
+        self.num_reduced_channels = input_channels if num_reduced_channels is None else num_reduced_channels
+        self.num_channels_of_local_aggregation = num_channels_of_local_aggregation
+        self.max_neighbour_distance = max_neighbor_distance
+        self.neighbor_nsample = neighbor_nsample
+        self.neighbor_type = neighbor_type  # 1: ball, others: cube
+
+        if self.local_aggregation_type == 'local_interpolation':
+            self.local_interpolate_module = VectorPoolLocalInterpolateModule(
+                mlp=None, num_voxels=self.num_local_voxel,
+                max_neighbour_distance=self.max_neighbour_distance,
+                nsample=self.neighbor_nsample,
+                neighbor_type=self.neighbor_type,
+                neighbour_distance_multiplier=neighbor_distance_multiplier,
+            )
+            num_c_in = (self.num_reduced_channels + 9) * self.total_voxels
+        else:
+            self.local_interpolate_module = None
+            num_c_in = (self.num_reduced_channels + 3) * self.total_voxels
+
+        num_c_out = self.total_voxels * self.num_channels_of_local_aggregation
+
+        self.separate_local_aggregation_layer = nn.Sequential(
+            nn.Conv1d(num_c_in, num_c_out, kernel_size=1, groups=self.total_voxels, bias=False),
+            nn.BatchNorm1d(num_c_out),
+            nn.ReLU()
+        )
+
+        post_mlp_list = []
+        c_in = num_c_out
+        for cur_num_c in post_mlps:
+            post_mlp_list.extend([
+                nn.Conv1d(c_in, cur_num_c, kernel_size=1, bias=False),
+                nn.BatchNorm1d(cur_num_c),
+                nn.ReLU()
+            ])
+            c_in = cur_num_c
+        self.post_mlps = nn.Sequential(*post_mlp_list)
+
+        self.num_mean_points_per_grid = 20
+        self.init_weights()
+
+    def init_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d) or isinstance(m, nn.Conv1d):
+                nn.init.kaiming_normal_(m.weight)
+                if m.bias is not None:
+                    nn.init.constant_(m.bias, 0)
+            if isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
+                nn.init.constant_(m.weight, 1.0)
+                nn.init.constant_(m.bias, 0)
+
+    def extra_repr(self) -> str:
+        ret = f'radius={self.max_neighbour_distance}, local_voxels=({self.num_local_voxel}, ' \
+              f'local_aggregation_type={self.local_aggregation_type}, ' \
+              f'num_c_reduction={self.input_channels}->{self.num_reduced_channels}, ' \
+              f'num_c_local_aggregation={self.num_channels_of_local_aggregation}'
+        return ret
+
+    def vector_pool_with_voxel_query(self, xyz, xyz_batch_cnt, features, new_xyz, new_xyz_batch_cnt):
+        use_xyz = 1
+        pooling_type = 0 if self.local_aggregation_type == 'voxel_avg_pool' else 1
+
+        new_features, new_local_xyz, num_mean_points_per_grid, point_cnt_of_grid = pointnet2_utils.vector_pool_with_voxel_query_op(
+            xyz, xyz_batch_cnt, features, new_xyz, new_xyz_batch_cnt,
+            self.num_local_voxel[0], self.num_local_voxel[1], self.num_local_voxel[2],
+            self.max_neighbour_distance, self.num_reduced_channels, use_xyz,
+            self.num_mean_points_per_grid, self.neighbor_nsample, self.neighbor_type,
+            pooling_type
+        )
+        self.num_mean_points_per_grid = max(self.num_mean_points_per_grid, num_mean_points_per_grid.item())
+
+        num_new_pts = new_features.shape[0]
+        new_local_xyz = new_local_xyz.view(num_new_pts, -1, 3)  # (N, num_voxel, 3)
+        new_features = new_features.view(num_new_pts, -1, self.num_reduced_channels)  # (N, num_voxel, C)
+        new_features = torch.cat((new_local_xyz, new_features), dim=-1).view(num_new_pts, -1)
+
+        return new_features, point_cnt_of_grid
+
+    @staticmethod
+    def get_dense_voxels_by_center(point_centers, max_neighbour_distance, num_voxels):
+        """
+        Args:
+            point_centers: (N, 3)
+            max_neighbour_distance: float
+            num_voxels: [num_x, num_y, num_z]
+
+        Returns:
+            voxel_centers: (N, total_voxels, 3)
+        """
+        R = max_neighbour_distance
+        device = point_centers.device
+        x_grids = torch.arange(-R + R / num_voxels[0], R - R / num_voxels[0] + 1e-5, 2 * R / num_voxels[0], device=device)
+        y_grids = torch.arange(-R + R / num_voxels[1], R - R / num_voxels[1] + 1e-5, 2 * R / num_voxels[1], device=device)
+        z_grids = torch.arange(-R + R / num_voxels[2], R - R / num_voxels[2] + 1e-5, 2 * R / num_voxels[2], device=device)
+        x_offset, y_offset, z_offset = torch.meshgrid(x_grids, y_grids, z_grids)  # shape: [num_x, num_y, num_z]
+        xyz_offset = torch.cat((
+            x_offset.contiguous().view(-1, 1),
+            y_offset.contiguous().view(-1, 1),
+            z_offset.contiguous().view(-1, 1)), dim=-1
+        )
+        voxel_centers = point_centers[:, None, :] + xyz_offset[None, :, :]
+        return voxel_centers
+
+    def vector_pool_with_local_interpolate(self, xyz, xyz_batch_cnt, features, new_xyz, new_xyz_batch_cnt):
+        """
+        Args:
+            xyz: (N, 3)
+            xyz_batch_cnt: (batch_size)
+            features: (N, C)
+            new_xyz: (M, 3)
+            new_xyz_batch_cnt: (batch_size)
+        Returns:
+            new_features: (M, total_voxels * C)
+        """
+        voxel_centers = self.get_dense_voxels_by_center(
+            point_centers=new_xyz, max_neighbour_distance=self.max_neighbour_distance, num_voxels=self.num_local_voxel
+        )  # (M1 + M2 + ..., total_voxels, 3)
+        voxel_features = self.local_interpolate_module.forward(
+            support_xyz=xyz, support_features=features, xyz_batch_cnt=xyz_batch_cnt,
+            new_xyz=new_xyz, new_xyz_grid_centers=voxel_centers, new_xyz_batch_cnt=new_xyz_batch_cnt
+        )  # ((M1 + M2 ...) * total_voxels, C)
+
+        voxel_features = voxel_features.contiguous().view(-1, self.total_voxels * voxel_features.shape[-1])
+        return voxel_features
+
+    def forward(self, xyz, xyz_batch_cnt, new_xyz, new_xyz_batch_cnt, features, **kwargs):
+        """
+        :param xyz: (N1 + N2 ..., 3) tensor of the xyz coordinates of the features
+        :param xyz_batch_cnt: (batch_size), [N1, N2, ...]
+        :param new_xyz: (M1 + M2 ..., 3)
+        :param new_xyz_batch_cnt: (batch_size), [M1, M2, ...]
+        :param features: (N1 + N2 ..., C) tensor of the descriptors of the the features
+        :return:
+            new_xyz: (M1 + M2 ..., 3) tensor of the new features' xyz
+            new_features: (M1 + M2 ..., \sum_k(mlps[k][-1])) tensor of the new_features descriptors
+        """
+        N, C = features.shape
+
+        assert C % self.num_reduced_channels == 0, \
+            f'the input channels ({C}) should be an integral multiple of num_reduced_channels({self.num_reduced_channels})'
+
+        features = features.view(N, -1, self.num_reduced_channels).sum(dim=1)
+
+        if self.local_aggregation_type in ['voxel_avg_pool', 'voxel_random_choice']:
+            vector_features, point_cnt_of_grid = self.vector_pool_with_voxel_query(
+                xyz=xyz, xyz_batch_cnt=xyz_batch_cnt, features=features,
+                new_xyz=new_xyz, new_xyz_batch_cnt=new_xyz_batch_cnt
+            )
+        elif self.local_aggregation_type == 'local_interpolation':
+            vector_features = self.vector_pool_with_local_interpolate(
+                xyz=xyz, xyz_batch_cnt=xyz_batch_cnt, features=features,
+                new_xyz=new_xyz, new_xyz_batch_cnt=new_xyz_batch_cnt
+            )  # (M1 + M2 + ..., total_voxels * C)
+        else:
+            raise NotImplementedError
+
+        vector_features = vector_features.permute(1, 0)[None, :, :]  # (1, num_voxels * C, M1 + M2 ...)
+
+        new_features = self.separate_local_aggregation_layer(vector_features)
+
+        new_features = self.post_mlps(new_features)
+        new_features = new_features.squeeze(dim=0).permute(1, 0)
+        return new_xyz, new_features
+
+
+class VectorPoolAggregationModuleMSG(nn.Module):
+    def __init__(self, input_channels, config):
+        super().__init__()
+        self.model_cfg = config
+        self.num_groups = self.model_cfg.NUM_GROUPS
+
+        self.layers = []
+        c_in = 0
+        for k in range(self.num_groups):
+            cur_config = self.model_cfg[f'GROUP_CFG_{k}']
+            cur_vector_pool_module = VectorPoolAggregationModule(
+                input_channels=input_channels, num_local_voxel=cur_config.NUM_LOCAL_VOXEL,
+                post_mlps=cur_config.POST_MLPS,
+                max_neighbor_distance=cur_config.MAX_NEIGHBOR_DISTANCE,
+                neighbor_nsample=cur_config.NEIGHBOR_NSAMPLE,
+                local_aggregation_type=self.model_cfg.LOCAL_AGGREGATION_TYPE,
+                num_reduced_channels=self.model_cfg.get('NUM_REDUCED_CHANNELS', None),
+                num_channels_of_local_aggregation=self.model_cfg.NUM_CHANNELS_OF_LOCAL_AGGREGATION,
+                neighbor_distance_multiplier=2.0
+            )
+            self.__setattr__(f'layer_{k}', cur_vector_pool_module)
+            c_in += cur_config.POST_MLPS[-1]
+
+        c_in += 3  # use_xyz
+
+        shared_mlps = []
+        for cur_num_c in self.model_cfg.MSG_POST_MLPS:
+            shared_mlps.extend([
+                nn.Conv1d(c_in, cur_num_c, kernel_size=1, bias=False),
+                nn.BatchNorm1d(cur_num_c),
+                nn.ReLU()
+            ])
+            c_in = cur_num_c
+        self.msg_post_mlps = nn.Sequential(*shared_mlps)
+
+    def forward(self, **kwargs):
+        features_list = []
+        for k in range(self.num_groups):
+            cur_xyz, cur_features = self.__getattr__(f'layer_{k}')(**kwargs)
+            features_list.append(cur_features)
+
+        features = torch.cat(features_list, dim=-1)
+        features = torch.cat((cur_xyz, features), dim=-1)
+        features = features.permute(1, 0)[None, :, :]  # (1, C, N)
+        new_features = self.msg_post_mlps(features)
+        new_features = new_features.squeeze(dim=0).permute(1, 0)  # (N, C)
+
+        return cur_xyz, new_features

pcdet/ops/pointnet2/pointnet2_stack/pointnet2_utils.py

@@ -155,7 +155,7 @@ class QueryAndGroup(nn.Module):
         return new_features, idx
 
 
-class FurthestPointSampling(Function):
+class FarthestPointSampling(Function):
     @staticmethod
     def forward(ctx, xyz: torch.Tensor, npoint: int):
         """
@@ -173,7 +173,7 @@ class FurthestPointSampling(Function):
         output = torch.cuda.IntTensor(B, npoint)
         temp = torch.cuda.FloatTensor(B, N).fill_(1e10)
 
-        pointnet2.furthest_point_sampling_wrapper(B, N, npoint, xyz, temp, output)
+        pointnet2.farthest_point_sampling_wrapper(B, N, npoint, xyz, temp, output)
         return output
 
     @staticmethod
@@ -181,7 +181,44 @@ class FurthestPointSampling(Function):
         return None, None
 
 
-furthest_point_sample = FurthestPointSampling.apply
+farthest_point_sample = furthest_point_sample = FarthestPointSampling.apply
+
+
+class StackFarthestPointSampling(Function):
+    @staticmethod
+    def forward(ctx, xyz, xyz_batch_cnt, npoint):
+        """
+        Args:
+            ctx:
+            xyz: (N1 + N2 + ..., 3) where N > npoint
+            xyz_batch_cnt: [N1, N2, ...]
+            npoint: int, number of features in the sampled set
+
+        Returns:
+            output: (npoint.sum()) tensor containing the set,
+            npoint: (M1, M2, ...)
+        """
+        assert xyz.is_contiguous() and xyz.shape[1] == 3
+
+        batch_size = xyz_batch_cnt.__len__()
+        if not isinstance(npoint, torch.Tensor):
+            if not isinstance(npoint, list):
+                npoint = [npoint for i in range(batch_size)]
+            npoint = torch.tensor(npoint, device=xyz.device).int()
+
+        N, _ = xyz.size()
+        temp = torch.cuda.FloatTensor(N).fill_(1e10)
+        output = torch.cuda.IntTensor(npoint.sum().item())
+
+        pointnet2.stack_farthest_point_sampling_wrapper(xyz, temp, xyz_batch_cnt, output, npoint)
+        return output
+
+    @staticmethod
+    def backward(xyz, a=None):
+        return None, None
+
+
+stack_farthest_point_sample = StackFarthestPointSampling.apply
 
 
 class ThreeNN(Function):
@@ -262,5 +299,154 @@ class ThreeInterpolate(Function):
 three_interpolate = ThreeInterpolate.apply
 
 
+class ThreeNNForVectorPoolByTwoStep(Function):
+    @staticmethod
+    def forward(ctx, support_xyz, xyz_batch_cnt, new_xyz, new_xyz_grid_centers, new_xyz_batch_cnt,
+                max_neighbour_distance, nsample, neighbor_type, avg_length_of_neighbor_idxs, num_total_grids,
+                neighbor_distance_multiplier):
+        """
+        Args:
+            ctx:
+            // support_xyz: (N1 + N2 ..., 3) xyz coordinates of the features
+            // xyz_batch_cnt: (batch_size), [N1, N2, ...]
+            // new_xyz: (M1 + M2 ..., 3) centers of the ball query
+            // new_xyz_grid_centers: (M1 + M2 ..., num_total_grids, 3) grids centers of each grid
+            // new_xyz_batch_cnt: (batch_size), [M1, M2, ...]
+            // nsample: find all (-1), find limited number(>0)
+            // neighbor_type: 1: ball, others: cube
+            // neighbor_distance_multiplier: query_distance = neighbor_distance_multiplier * max_neighbour_distance
+
+        Returns:
+            // new_xyz_grid_idxs: (M1 + M2 ..., num_total_grids, 3) three-nn
+            // new_xyz_grid_dist2: (M1 + M2 ..., num_total_grids, 3) square of dist of three-nn
+        """
+        num_new_xyz = new_xyz.shape[0]
+        new_xyz_grid_dist2 = new_xyz_grid_centers.new_zeros(new_xyz_grid_centers.shape)
+        new_xyz_grid_idxs = new_xyz_grid_centers.new_zeros(new_xyz_grid_centers.shape).int().fill_(-1)
+
+        while True:
+            num_max_sum_points = avg_length_of_neighbor_idxs * num_new_xyz
+            stack_neighbor_idxs = new_xyz_grid_idxs.new_zeros(num_max_sum_points)
+            start_len = new_xyz_grid_idxs.new_zeros(num_new_xyz, 2).int()
+            cumsum = new_xyz_grid_idxs.new_zeros(1)
+
+            pointnet2.query_stacked_local_neighbor_idxs_wrapper_stack(
+                support_xyz.contiguous(), xyz_batch_cnt.contiguous(),
+                new_xyz.contiguous(), new_xyz_batch_cnt.contiguous(),
+                stack_neighbor_idxs.contiguous(), start_len.contiguous(), cumsum,
+                avg_length_of_neighbor_idxs, max_neighbour_distance * neighbor_distance_multiplier,
+                nsample, neighbor_type
+            )
+            avg_length_of_neighbor_idxs = cumsum[0].item() // num_new_xyz + int(cumsum[0].item() % num_new_xyz > 0)
+
+            if cumsum[0] <= num_max_sum_points:
+                break
+
+        stack_neighbor_idxs = stack_neighbor_idxs[:cumsum[0]]
+        pointnet2.query_three_nn_by_stacked_local_idxs_wrapper_stack(
+            support_xyz, new_xyz, new_xyz_grid_centers, new_xyz_grid_idxs, new_xyz_grid_dist2,
+            stack_neighbor_idxs, start_len, num_new_xyz, num_total_grids
+        )
+
+        return torch.sqrt(new_xyz_grid_dist2), new_xyz_grid_idxs, torch.tensor(avg_length_of_neighbor_idxs)
+
+
+three_nn_for_vector_pool_by_two_step = ThreeNNForVectorPoolByTwoStep.apply
+
+
+class VectorPoolWithVoxelQuery(Function):
+    @staticmethod
+    def forward(ctx, support_xyz: torch.Tensor, xyz_batch_cnt: torch.Tensor, support_features: torch.Tensor,
+                new_xyz: torch.Tensor, new_xyz_batch_cnt: torch.Tensor, num_grid_x, num_grid_y, num_grid_z,
+                max_neighbour_distance, num_c_out_each_grid, use_xyz,
+                num_mean_points_per_grid=100, nsample=-1, neighbor_type=0, pooling_type=0):
+        """
+        Args:
+            ctx:
+            support_xyz: (N1 + N2 ..., 3) xyz coordinates of the features
+            xyz_batch_cnt: (batch_size), [N1, N2, ...]
+            support_features: (N1 + N2 ..., C)
+            new_xyz: (M1 + M2 ..., 3) centers of new positions
+            new_xyz_batch_cnt: (batch_size), [M1, M2, ...]
+            num_grid_x: number of grids in each local area centered at new_xyz
+            num_grid_y:
+            num_grid_z:
+            max_neighbour_distance:
+            num_c_out_each_grid:
+            use_xyz:
+            neighbor_type: 1: ball, others: cube:
+            pooling_type: 0: avg_pool, 1: random choice
+        Returns:
+            new_features: (M1 + M2 ..., num_c_out)
+        """
+        assert support_xyz.is_contiguous()
+        assert support_features.is_contiguous()
+        assert xyz_batch_cnt.is_contiguous()
+        assert new_xyz.is_contiguous()
+        assert new_xyz_batch_cnt.is_contiguous()
+        num_total_grids = num_grid_x * num_grid_y * num_grid_z
+        num_c_out = num_c_out_each_grid * num_total_grids
+        N, num_c_in = support_features.shape
+        M = new_xyz.shape[0]
+
+        assert num_c_in % num_c_out_each_grid == 0, \
+            f'the input channels ({num_c_in}) should be an integral multiple of num_c_out_each_grid({num_c_out_each_grid})'
+
+        while True:
+            new_features = support_features.new_zeros((M, num_c_out))
+            new_local_xyz = support_features.new_zeros((M, 3 * num_total_grids))
+            point_cnt_of_grid = xyz_batch_cnt.new_zeros((M, num_total_grids))
+
+            num_max_sum_points = num_mean_points_per_grid * M
+            grouped_idxs = xyz_batch_cnt.new_zeros((num_max_sum_points, 3))
+
+            num_cum_sum = pointnet2.vector_pool_wrapper(
+                support_xyz, xyz_batch_cnt, support_features, new_xyz, new_xyz_batch_cnt,
+                new_features, new_local_xyz, point_cnt_of_grid, grouped_idxs,
+                num_grid_x, num_grid_y, num_grid_z, max_neighbour_distance, use_xyz,
+                num_max_sum_points, nsample, neighbor_type, pooling_type
+            )
+            num_mean_points_per_grid = num_cum_sum // M + int(num_cum_sum % M > 0)
+            if num_cum_sum <= num_max_sum_points:
+                break
+
+        grouped_idxs = grouped_idxs[:num_cum_sum]
+
+        normalizer = torch.clamp_min(point_cnt_of_grid[:, :, None].float(), min=1e-6)
+        new_features = (new_features.view(-1, num_total_grids, num_c_out_each_grid) / normalizer).view(-1, num_c_out)
+
+        if use_xyz:
+            new_local_xyz = (new_local_xyz.view(-1, num_total_grids, 3) / normalizer).view(-1, num_total_grids * 3)
+
+        num_mean_points_per_grid = torch.Tensor([num_mean_points_per_grid]).int()
+        nsample = torch.Tensor([nsample]).int()
+        ctx.vector_pool_for_backward = (point_cnt_of_grid, grouped_idxs, N, num_c_in)
+        ctx.mark_non_differentiable(new_local_xyz, num_mean_points_per_grid, nsample, point_cnt_of_grid)
+        return new_features, new_local_xyz, num_mean_points_per_grid, point_cnt_of_grid
+
+    @staticmethod
+    def backward(ctx, grad_new_features: torch.Tensor, grad_local_xyz: torch.Tensor, grad_num_cum_sum, grad_point_cnt_of_grid):
+        """
+        Args:
+            ctx:
+            grad_new_features: (M1 + M2 ..., num_c_out), num_c_out = num_c_out_each_grid * num_total_grids
+
+        Returns:
+            grad_support_features: (N1 + N2 ..., C_in)
+        """
+        point_cnt_of_grid, grouped_idxs, N, num_c_in = ctx.vector_pool_for_backward
+        grad_support_features = grad_new_features.new_zeros((N, num_c_in))
+
+        pointnet2.vector_pool_grad_wrapper(
+            grad_new_features.contiguous(), point_cnt_of_grid, grouped_idxs,
+            grad_support_features
+        )
+
+        return None, None, grad_support_features, None, None, None, None, None, None, None, None, None, None, None, None
+
+
+vector_pool_with_voxel_query_op = VectorPoolWithVoxelQuery.apply
+
+
 if __name__ == '__main__':
     pass

pcdet/ops/pointnet2/pointnet2_stack/src/pointnet2_api.cpp

@@ -6,13 +6,15 @@
 #include "sampling_gpu.h"
 #include "interpolate_gpu.h"
 #include "voxel_query_gpu.h"
+#include "vector_pool_gpu.h"
 
 
 PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
     m.def("ball_query_wrapper", &ball_query_wrapper_stack, "ball_query_wrapper_stack");
     m.def("voxel_query_wrapper", &voxel_query_wrapper_stack, "voxel_query_wrapper_stack");
 
-    m.def("furthest_point_sampling_wrapper", &furthest_point_sampling_wrapper, "furthest_point_sampling_wrapper");
+    m.def("farthest_point_sampling_wrapper", &farthest_point_sampling_wrapper, "farthest_point_sampling_wrapper");
+    m.def("stack_farthest_point_sampling_wrapper", &stack_farthest_point_sampling_wrapper, "stack_farthest_point_sampling_wrapper");
 
     m.def("group_points_wrapper", &group_points_wrapper_stack, "group_points_wrapper_stack");
     m.def("group_points_grad_wrapper", &group_points_grad_wrapper_stack, "group_points_grad_wrapper_stack");
@@ -20,4 +22,10 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
     m.def("three_nn_wrapper", &three_nn_wrapper_stack, "three_nn_wrapper_stack");
     m.def("three_interpolate_wrapper", &three_interpolate_wrapper_stack, "three_interpolate_wrapper_stack");
     m.def("three_interpolate_grad_wrapper", &three_interpolate_grad_wrapper_stack, "three_interpolate_grad_wrapper_stack");
+
+    m.def("query_stacked_local_neighbor_idxs_wrapper_stack", &query_stacked_local_neighbor_idxs_wrapper_stack, "query_stacked_local_neighbor_idxs_wrapper_stack");
+    m.def("query_three_nn_by_stacked_local_idxs_wrapper_stack", &query_three_nn_by_stacked_local_idxs_wrapper_stack, "query_three_nn_by_stacked_local_idxs_wrapper_stack");
+
+    m.def("vector_pool_wrapper", &vector_pool_wrapper_stack, "vector_pool_grad_wrapper_stack");
+    m.def("vector_pool_grad_wrapper", &vector_pool_grad_wrapper_stack, "vector_pool_grad_wrapper_stack");
 }

pcdet/ops/pointnet2/pointnet2_stack/src/sampling.cpp

@@ -21,7 +21,7 @@ extern THCState *state;
 #define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x)
 
 
-int furthest_point_sampling_wrapper(int b, int n, int m,
+int farthest_point_sampling_wrapper(int b, int n, int m,
     at::Tensor points_tensor, at::Tensor temp_tensor, at::Tensor idx_tensor) {
 
     CHECK_INPUT(points_tensor);
@@ -32,6 +32,29 @@ int furthest_point_sampling_wrapper(int b, int n, int m,
     float *temp = temp_tensor.data<float>();
     int *idx = idx_tensor.data<int>();
 
-    furthest_point_sampling_kernel_launcher(b, n, m, points, temp, idx);
+    farthest_point_sampling_kernel_launcher(b, n, m, points, temp, idx);
     return 1;
 }
+
+
+int stack_farthest_point_sampling_wrapper(at::Tensor points_tensor,
+  at::Tensor temp_tensor, at::Tensor xyz_batch_cnt_tensor, at::Tensor idx_tensor,
+  at::Tensor num_sampled_points_tensor) {
+
+    CHECK_INPUT(points_tensor);
+    CHECK_INPUT(temp_tensor);
+    CHECK_INPUT(idx_tensor);
+    CHECK_INPUT(xyz_batch_cnt_tensor);
+    CHECK_INPUT(num_sampled_points_tensor);
+
+    int batch_size = xyz_batch_cnt_tensor.size(0);
+    int N = points_tensor.size(0);
+    const float *points = points_tensor.data<float>();
+    float *temp = temp_tensor.data<float>();
+    int *xyz_batch_cnt = xyz_batch_cnt_tensor.data<int>();
+    int *idx = idx_tensor.data<int>();
+    int *num_sampled_points = num_sampled_points_tensor.data<int>();
+
+    stack_farthest_point_sampling_kernel_launcher(N, batch_size, points, temp, xyz_batch_cnt, idx, num_sampled_points);
+    return 1;
+}
\ No newline at end of file

pcdet/ops/pointnet2/pointnet2_stack/src/sampling_gpu.cu

@@ -22,7 +22,7 @@ __device__ void __update(float *__restrict__ dists, int *__restrict__ dists_i, i
 
 
 template <unsigned int block_size>
-__global__ void furthest_point_sampling_kernel(int b, int n, int m,
+__global__ void farthest_point_sampling_kernel(int b, int n, int m,
     const float *__restrict__ dataset, float *__restrict__ temp, int *__restrict__ idxs) {
     // dataset: (B, N, 3)
     // tmp: (B, N)
@@ -139,7 +139,7 @@ __global__ void furthest_point_sampling_kernel(int b, int n, int m,
     }
 }
 
-void furthest_point_sampling_kernel_launcher(int b, int n, int m,
+void farthest_point_sampling_kernel_launcher(int b, int n, int m,
     const float *dataset, float *temp, int *idxs) {
     // dataset: (B, N, 3)
     // tmp: (B, N)
@@ -151,29 +151,29 @@ void furthest_point_sampling_kernel_launcher(int b, int n, int m,
 
     switch (n_threads) {
         case 1024:
-        furthest_point_sampling_kernel<1024><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        farthest_point_sampling_kernel<1024><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
         case 512:
-        furthest_point_sampling_kernel<512><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        farthest_point_sampling_kernel<512><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
         case 256:
-        furthest_point_sampling_kernel<256><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        farthest_point_sampling_kernel<256><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
         case 128:
-        furthest_point_sampling_kernel<128><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        farthest_point_sampling_kernel<128><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
         case 64:
-        furthest_point_sampling_kernel<64><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        farthest_point_sampling_kernel<64><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
         case 32:
-        furthest_point_sampling_kernel<32><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        farthest_point_sampling_kernel<32><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
         case 16:
-        furthest_point_sampling_kernel<16><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        farthest_point_sampling_kernel<16><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
         case 8:
-        furthest_point_sampling_kernel<8><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        farthest_point_sampling_kernel<8><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
         case 4:
-        furthest_point_sampling_kernel<4><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        farthest_point_sampling_kernel<4><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
         case 2:
-        furthest_point_sampling_kernel<2><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        farthest_point_sampling_kernel<2><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
         case 1:
-        furthest_point_sampling_kernel<1><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
+        farthest_point_sampling_kernel<1><<<b, n_threads>>>(b, n, m, dataset, temp, idxs); break;
         default:
-        furthest_point_sampling_kernel<512><<<b, n_threads>>>(b, n, m, dataset, temp, idxs);
+        farthest_point_sampling_kernel<512><<<b, n_threads>>>(b, n, m, dataset, temp, idxs);
     }
 
     err = cudaGetLastError();
@@ -182,3 +182,168 @@ void furthest_point_sampling_kernel_launcher(int b, int n, int m,
         exit(-1);
     }
 }
+
+
+template <unsigned int block_size>
+__global__ void stack_farthest_point_sampling_kernel(int batch_size, int N,
+    const float *dataset, float *temp, int *xyz_batch_cnt, int *idxs, int *num_sampled_points) {
+    // """
+    // Args:
+    //     ctx:
+    //     dataset: (N1 + N2 + ..., 3) where N > npoint
+    //     temp: (N1 + N2 + ...) where N > npoint
+    //     xyz_batch_cnt: [N1, N2, ...]
+    //     num_sampled_points: [M1, M2, ...] int, number of features in the sampled set
+
+    // Returns:
+    //     idxs: (npoint.sum()) tensor containing the set,
+    //     npoint: (M1, M2, ...)
+    // """
+
+    __shared__ float dists[block_size];
+    __shared__ int dists_i[block_size];
+
+    int bs_idx = blockIdx.x;
+
+    int xyz_batch_start_idx = 0, idxs_start_idx = 0;
+    for (int k = 0; k < bs_idx; k++){
+        xyz_batch_start_idx += xyz_batch_cnt[k];
+        idxs_start_idx += num_sampled_points[k];
+    }
+
+    dataset += xyz_batch_start_idx * 3;
+    temp += xyz_batch_start_idx;
+    idxs += idxs_start_idx;
+
+    int n = xyz_batch_cnt[bs_idx];
+    int m = num_sampled_points[bs_idx];
+
+    int tid = threadIdx.x;
+    const int stride = block_size;
+
+    int old = 0;
+    if (threadIdx.x == 0) idxs[0] = xyz_batch_start_idx;
+
+    __syncthreads();
+    for (int j = 1; j < m; j++) {
+        int besti = 0;
+        float best = -1;
+        float x1 = dataset[old * 3 + 0];
+        float y1 = dataset[old * 3 + 1];
+        float z1 = dataset[old * 3 + 2];
+        for (int k = tid; k < n; k += stride) {
+            float x2, y2, z2;
+            x2 = dataset[k * 3 + 0];
+            y2 = dataset[k * 3 + 1];
+            z2 = dataset[k * 3 + 2];
+            // float mag = (x2 * x2) + (y2 * y2) + (z2 * z2);
+            // if (mag <= 1e-3)
+            // continue;
+
+            float d = (x2 - x1) * (x2 - x1) + (y2 - y1) * (y2 - y1) + (z2 - z1) * (z2 - z1);
+            float d2 = min(d, temp[k]);
+            temp[k] = d2;
+            besti = d2 > best ? k : besti;
+            best = d2 > best ? d2 : best;
+        }
+        dists[tid] = best;
+        dists_i[tid] = besti;
+        __syncthreads();
+
+        if (block_size >= 1024) {
+            if (tid < 512) {
+                __update(dists, dists_i, tid, tid + 512);
+            }
+            __syncthreads();
+        }
+
+        if (block_size >= 512) {
+            if (tid < 256) {
+                __update(dists, dists_i, tid, tid + 256);
+            }
+            __syncthreads();
+        }
+        if (block_size >= 256) {
+            if (tid < 128) {
+                __update(dists, dists_i, tid, tid + 128);
+            }
+            __syncthreads();
+        }
+        if (block_size >= 128) {
+            if (tid < 64) {
+                __update(dists, dists_i, tid, tid + 64);
+            }
+            __syncthreads();
+        }
+        if (block_size >= 64) {
+            if (tid < 32) {
+                __update(dists, dists_i, tid, tid + 32);
+            }
+            __syncthreads();
+        }
+        if (block_size >= 32) {
+            if (tid < 16) {
+                __update(dists, dists_i, tid, tid + 16);
+            }
+            __syncthreads();
+        }
+        if (block_size >= 16) {
+            if (tid < 8) {
+                __update(dists, dists_i, tid, tid + 8);
+            }
+            __syncthreads();
+        }
+        if (block_size >= 8) {
+            if (tid < 4) {
+                __update(dists, dists_i, tid, tid + 4);
+            }
+            __syncthreads();
+        }
+        if (block_size >= 4) {
+            if (tid < 2) {
+                __update(dists, dists_i, tid, tid + 2);
+            }
+            __syncthreads();
+        }
+        if (block_size >= 2) {
+            if (tid < 1) {
+                __update(dists, dists_i, tid, tid + 1);
+            }
+            __syncthreads();
+        }
+
+        old = dists_i[0];
+        if (tid == 0)
+            idxs[j] = old + xyz_batch_start_idx;
+    }
+}
+
+
+void stack_farthest_point_sampling_kernel_launcher(int N, int batch_size,
+    const float *dataset, float *temp, int *xyz_batch_cnt, int *idxs, int *num_sampled_points) {
+    // """
+    // Args:
+    //     ctx:
+    //     dataset: (N1 + N2 + ..., 3) where N > npoint
+    //     temp: (N1 + N2 + ...) where N > npoint
+    //     xyz_batch_cnt: [N1, N2, ...]
+    //     npoint: int, number of features in the sampled set
+
+    // Returns:
+    //     idxs: (npoint.sum()) tensor containing the set,
+    //     npoint: (M1, M2, ...)
+    // """
+
+    cudaError_t err;
+    unsigned int n_threads = opt_n_threads(N);
+
+    stack_farthest_point_sampling_kernel<1024><<<batch_size, 1024>>>(
+        batch_size, N, dataset, temp, xyz_batch_cnt, idxs, num_sampled_points
+    );
+
+    err = cudaGetLastError();
+    if (cudaSuccess != err) {
+        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+        exit(-1);
+    }
+}
\ No newline at end of file

pcdet/ops/pointnet2/pointnet2_stack/src/sampling_gpu.h

@@ -6,10 +6,18 @@
 #include<vector>
 
 
-int furthest_point_sampling_wrapper(int b, int n, int m,
+int farthest_point_sampling_wrapper(int b, int n, int m,
     at::Tensor points_tensor, at::Tensor temp_tensor, at::Tensor idx_tensor);
 
-void furthest_point_sampling_kernel_launcher(int b, int n, int m,
+void farthest_point_sampling_kernel_launcher(int b, int n, int m,
     const float *dataset, float *temp, int *idxs);
 
+int stack_farthest_point_sampling_wrapper(
+    at::Tensor points_tensor, at::Tensor temp_tensor, at::Tensor xyz_batch_cnt_tensor,
+    at::Tensor idx_tensor, at::Tensor num_sampled_points_tensor);
+
+
+void stack_farthest_point_sampling_kernel_launcher(int N, int batch_size,
+    const float *dataset, float *temp, int *xyz_batch_cnt, int *idxs, int *num_sampled_points);
+
 #endif