We already support to use all the optimizers implemented by PyTorch, and the only modification is to change the `optimizer` field in `optim_wrapper` field of config files. For example, if you want to use `ADAM` (note that the performance could drop a lot), the modification could be as the following.
...
...
@@ -192,7 +192,7 @@ Tricks not implemented by the optimizer should be implemented through optimizer
## Customize training schedules
By default we use step learning rate with 1x schedule, this calls [MultiStepLR](https://github.com/open-mmlab/mmengine/blob/main/mmengine/optim/scheduler/lr_scheduler.py#L139) in MMEngine.
By default we use step learning rate with 1x schedule, this calls [`MultiStepLR`](https://github.com/open-mmlab/mmengine/blob/main/mmengine/optim/scheduler/lr_scheduler.py#L139) in MMEngine.
We support many other learning rate schedule [here](https://github.com/open-mmlab/mmengine/blob/main/mmengine/optim/scheduler/lr_scheduler.py), such as `CosineAnnealingLR` and `PolyLR` schedules. Here are some examples
- Poly schedule:
...
...
@@ -219,7 +219,6 @@ We support many other learning rate schedule [here](https://github.com/open-mmla
begin=0,
end=8,
by_epoch=True)]
```
## Customize train loop
...
...
@@ -257,7 +256,9 @@ MMEngine provides many useful [hooks](https://github.com/open-mmlab/mmengine/blo
Here we give an example of creating a new hook in mmdet3d and using it in training.
```python
frommmengine.hooksimportHOOKS,Hook
frommmengine.hooksimportHook
frommmdet3d.registryimportHOOKS
@HOOKS.register_module()
...
...
@@ -341,7 +342,7 @@ There are some common hooks that are registered through `default_hooks`, they ar
-`CheckpointHook`: A hook that saves checkpoints periodically.
-`DistSamplerSeedHook`: A hook that sets the seed for sampler and batch_sampler.
`IterTimerHook`, `ParamSchedulerHook` and `DistSamplerSeedHook` are simple and no need to be modified usually, so here we reveals how what we can do with `LoggerHook`, `CheckpointHook` and `DetVisualizationHook`.
`IterTimerHook`, `ParamSchedulerHook` and `DistSamplerSeedHook` are simple and no need to be modified usually, so here we reveals how what we can do with `LoggerHook`, `CheckpointHook` and `Det3DVisualizationHook`.
To create KITTI point cloud data, we load the raw point cloud data and generate the relevant annotations including object labels and bounding boxes. We also generate all single training objects' point cloud in KITTI dataset and save them as `.bin` files in `data/kitti/kitti_gt_database`. Meanwhile, `.pkl` info files are also generated for training or validation. Subsequently, create KITTI data by running
To create KITTI point cloud data, we load the raw point cloud data and generate the relevant annotations including object labels and bounding boxes. We also generate all single training objects' point cloud in KITTI dataset and save them as `.bin` files in `data/kitti/kitti_gt_database`. Meanwhile, `.pkl` info files are also generated for training or validation. Subsequently, create KITTI data by running:
Note that if your local disk does not have enough space for saving converted data, you can change the `out-dir` to anywhere else, and you need to remove the `--with-plane` flag if `planes` are not prepared.
Note that if your local disk does not have enough space for saving converted data, you can change the `--out-dir` to anywhere else, and you need to remove the `--with-plane` flag if `planes` are not prepared.
The folder structure after processing should be as below
...
...
@@ -79,51 +78,51 @@ kitti
├── kitti_infos_trainval.pkl
```
-`kitti_gt_database/xxxxx.bin`: point cloud data included in each 3D bounding box of the training dataset
-`kitti_infos_train.pkl`: training dataset info, each frame info has two keys: `metainfo` and `data_list`.
`metainfo` is a dict, it contains the essential information for the dataset, such as `CLASSES` and `version`.
`data_list` is a list, it has all the needed data information, and each item is detailed information dict for a single sample. Detailed information is as follows:
-`kitti_gt_database/xxxxx.bin`: point cloud data included in each 3D bounding box of the training dataset.
-`kitti_infos_train.pkl`: training dataset, a dict contains two keys: `metainfo` and `data_list`.
`metainfo` contains the basic information for the dataset itself, such as `categories`, `dataset` and `info_version`, while `data_list` is a list of dict, each dict (hereinafter referred to as `info`) contains all the detailed information of single sample as follows:
- info\['sample_idx'\]: The index of this sample in the whole dataset.
- info\['images'\]: Information of images captured by multiple cameras. A dict contains five keys including: `CAM0`, `CAM1`, `CAM2`, `CAM3`, `R0_rect`.
- info\['images'\]\['R0_rect'\]: Rectifying rotation matrix with shape (4, 4).
- info\['images'\]\['CAM2'\]: Include some information about the `CAM2` camera sensor.
- info\['images'\]\['CAM2'\]\['img_path'\]: The path to the image file.
- info\['images'\]\['CAM2'\]\['img_path'\]: The filename of the image.
- info\['images'\]\['CAM2'\]\['height'\]: The height of the image.
- info\['images'\]\['CAM2'\]\['width'\]: The width of the image.
- info\['images'\]\['CAM2'\]\['cam2img'\]: Transformation matrix from camera to image with shape (4, 4).
- info\['images'\]\['CAM2'\]\['lidar2cam'\]: Transformation matrix from lidar to camera with shape (4, 4).
- info\['images'\]\['CAM2'\]\['lidar2img'\]: Transformation matrix from lidar to image with shape (4, 4).
- info\['lidar_points'\]: Information of point cloud captured by Lidar. A dict contains information of LiDAR point cloud frame.
- info\['lidar_points'\]\['lidar_path'\]: The file path of the lidar point cloud data.
- info\['lidar_points'\]\['num_features'\]: Number of features for each point.
- info\['lidar_points'\]: A dict containing all the information related to the lidar points.
- info\['lidar_points'\]\['lidar_path'\]: The filename of the lidar point cloud data.
- info\['lidar_points'\]\['num_pts_feats'\]: The feature dimension of point.
- info\['lidar_points'\]\['Tr_velo_to_cam'\]: Transformation from Velodyne coordinate to camera coordinate with shape (4, 4).
- info\['lidar_points'\]\['Tr_imu_to_velo'\]: Transformation from IMU coordinate to Velodyne coordinate with shape (4, 4).
- info\['instances'\]: Required by object detection task. A list contains some dict of instance infos. Each dict corresponds to annotations of one instance in this frame.
- info\['instances'\]\['bbox'\]: List of 4 numbers representing the 2D bounding box of the instance, in (x1, y1, x2, y2) order.
- info\['instances'\]\['bbox_3d'\]: List of 7 numbers representing the 3D bounding box of the instance, in (x, y, z, w, h, l, yaw) order.
- info\['instances'\]\['bbox_label'\]: An int indicate the 2D label of instance and the -1 indicating ignore.
- info\['instances'\]\['bbox_label_3d'\]: An int indicate the 3D label of instance and the -1 indicating ignore.
- info\['instances'\]\['depth'\]: Projected center depth of the 3D bounding box with respect to the image plane.
- info\['instances'\]\['num_lidar_pts'\]: The number of LiDAR points in the 3D bounding box.
- info\['instances'\]\['center_2d'\]: Projected 2D center of the 3D bounding box.
- info\['instances'\]\[i\]\['truncated'\]: Float from 0 (non-truncated) to 1 (truncated), where truncated refers to the object leaving image boundaries.
Please refer to [kitti_converter.py](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/tools/dataset_converters/kitti_converter.py) and [update_infos_to_v2.py ](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/tools/dataset_converters/update_infos_to_v2.py) for more details.
## Train pipeline
A typical train pipeline of 3D detection on KITTI is as below.
A typical train pipeline of 3D detection on KITTI is as below:
An example to test PointPillars on KITTI with 8 GPUs and generate a submission to the leaderboard. An example to test PointPillars on KITTI with 8 GPUs and generate a submission to the leaderboard is as follows:
An example to test PointPillars on KITTI with 8 GPUs and generate a submission to the leaderboard is as follows:
- First, you need to modify the `test_evaluator` dict in your config file to add `pklfile_prefix` and `submission_prefix`, just like:
- First, you need to modify the `test_dataloader` and `test_evaluator` dict in your config file, just like:
- Or you can use `--cfg-options "test_evaluator.jsonfile_prefix=work_dirs/pp-nus/results_eval.json)` after the test command, and run test script directly.
After generating `results/kitti-3class/kitti_results/xxxxx.txt` files, you can submit these files to KITTI benchmark. Please refer to the [KITTI official website](http://www.cvlibs.net/datasets/kitti/index.php) for more details.
@@ -40,8 +40,8 @@ Note that we follow the original folder names for clear organization. Please ren
## Dataset Preparation
The way to organize Lyft dataset is similar to nuScenes. We also generate the .pkl and .json files which share almost the same structure.
Next, we will mainly focus on the difference between these two datasets. For a more detailed explanation of the info structure, please refer to [nuScenes tutorial](https://github.com/open-mmlab/mmdetection3d/blob/master/docs/en/datasets/nuscenes_det.md).
The way to organize Lyft dataset is similar to nuScenes. We also generate the `.pkl` files which share almost the same structure.
Next, we will mainly focus on the difference between these two datasets. For a more detailed explanation of the info structure, please refer to [nuScenes tutorial](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/docs/en/advanced_guides/datasets/nuscenes_det.md).
To prepare info files for Lyft, run the following commands:
...
...
@@ -81,43 +81,44 @@ mmdetection3d
```
-`lyft_infos_train.pkl`: training dataset, a dict contains two keys: `metainfo` and `data_list`.
`metainfo` contains the basic information for the dataset itself, such as `CLASSES` and `version`, while `data_list` is a list of dict, each dict (hereinafter referred to as`info`) contains all the detailed information of single sample as follows:
`metainfo` contains the basic information for the dataset itself, such as `categories`, `dataset` and `info_version`, while `data_list` is a list of dict, each dict (hereinafter referred to as`info`) contains all the detailed information of single sample as follows:
- info\['sample_idx'\]: The index of this sample in the whole dataset.
- info\['token'\]: Sample data token.
- info\['timestamp'\]: Timestamp of the sample data.
- info\['lidar_points'\]: A dict contains all the information related to the lidar points.
- info\['lidar_points'\]: A dict containing all the information related to the lidar points.
- info\['lidar_points'\]\['lidar_path'\]: The filename of the lidar point cloud data.
- info\['lidar_points'\]\['num_pts_feats'\]: The feature dimension of point.
- info\['lidar_points'\]\['lidar2ego'\]: The transformation matrix from this lidar sensor to ego vehicle. (4x4 list)
- info\['lidar_points'\]\['ego2global'\]: The transformation matrix from the ego vehicle to global coordinates. (4x4 list)
- info\['lidar_sweeps'\]: A list contains sweeps information (The intermediate lidar frames without annotations)
- info\['lidar_sweeps'\]: A list contains sweeps information (The intermediate lidar frames without annotations).
- info\['lidar_sweeps'\]\[i\]\['lidar_points'\]\['data_path'\]: The lidar data path of i-th sweep.
- info\['lidar_sweeps'\]\[i\]\['lidar_points'\]\['lidar2ego'\]: The transformation matrix from this lidar sensor to ego vehicle in i-th sweep timestamp
- info\['lidar_sweeps'\]\[i\]\['lidar_points'\]\['ego2global'\]: The transformation matrix from the ego vehicle in i-th sweep timestamp to global coordinates. (4x4 list)
- info\['lidar_sweeps'\]\[i\]\['lidar2sensor'\]: The transformation matrix from the the lidar (for collecting the i-th sweep data) to the lidar collecting the key/sample data. (4x4 list)
- info\['lidar_sweeps'\]\[i\]\['lidar2sensor'\]: The transformation matrix from the keyframe lidar to the i-th frame lidar. (4x4 list)
- info\['lidar_sweeps'\]\[i\]\['timestamp'\]: Timestamp of the sweep data.
- info\['lidar_sweeps'\]\[i\]\['sample_data_token'\]: The sweep sample data token.
- info\['images'\]: A dict contains six keys corresponding to each camera: `'CAM_FRONT'`, `'CAM_FRONT_RIGHT'`, `'CAM_FRONT_LEFT'`, `'CAM_BACK'`, `'CAM_BACK_LEFT'`, `'CAM_BACK_RIGHT'`. Each dict contains all data information related to corresponding camera.
- info\['images'\]\['CAM_XXX'\]\['img_path'\]: Filename of image.
- info\['images'\]\['CAM_XXX'\]\['img_path'\]: The filename of the image.
- info\['images'\]\['CAM_XXX'\]\['cam2img'\]: The transformation matrix recording the intrinsic parameters when projecting 3D points to each image plane. (3x3 list)
- info\['images'\]\['CAM_XXX'\]\['sample_data_token'\]: Sample data token of image.
- info\['images'\]\['CAM_XXX'\]\['timestamp'\]: Timestamp of the image.
- info\['images'\]\['CAM_XXX'\]\['cam2ego'\]: The transformation matrix from this camera sensor to ego vehicle. (4x4 list)
- info\['images'\]\['CAM_XXX'\]\['lidar2cam'\]: The transformation matrix from lidar sensor to this camera. (4x4 list)
- info\['instances'\]: It is a list of dict. Each dict contains all annotation information of single instance.
- info\['instances'\]\['bbox_3d'\]: List of 7 numbers representing the 3D bounding box in lidar coordinate system of the instance, in (x, y, z, l, w, h, yaw) order.
- info\['instances'\]\['bbox_label_3d'\]: A int starting from 0 indicates the label of instance, while the -1 indicates ignore class.
- info\['instances'\]\['bbox_3d_isvalid'\]: Whether each bounding box is valid. In general, we only take the 3D boxes that include at least one lidar or radar point as valid boxes.
- info\['instances'\]: It is a list of dict. Each dict contains all annotation information of single instance. For the i-th instance:
- info\['instances'\]\[i\]\['bbox_3d'\]: List of 7 numbers representing the 3D bounding box in lidar coordinate system of the instance, in (x, y, z, l, w, h, yaw) order.
- info\['instances'\]\[i\]\['bbox_label_3d'\]: A int starting from 0 indicates the label of instance, while the -1 indicates ignore class.
- info\['instances'\]\[i\]\['bbox_3d_isvalid'\]: Whether each bounding box is valid. In general, we only take the 3D boxes that include at least one lidar or radar point as valid boxes.
Next, we will elaborate on the difference compared to nuScenes in terms of the details recorded in these info files.
-without `lyft_database/xxxxx.bin`: This folder and `.bin` files are not extracted on the Lyft dataset due to the negligible effect of ground-truth sampling in the experiments.
-Without `lyft_database/xxxxx.bin`: This folder and `.bin` files are not extracted on the Lyft dataset due to the negligible effect of ground-truth sampling in the experiments.
-`lyft_infos_train.pkl`:
- Without info\['instances'\]\['velocity'\], There is no velocity measurement on Lyft.
- Without info\['instances'\]\['num_lidar_pts'\] and info\['instances'\]\['num_radar_pts'\]
- Without info\['instances'\]\[i\]\['velocity'\]: There is no velocity measurement on Lyft.
- Without info\['instances'\]\[i\]\['num_lidar_pts'\] and info\['instances'\]\['num_radar_pts'\]
Here we only explain the data recorded in the training info files. The same applies to the validation set and test set(without instances).
Here we only explain the data recorded in the training info files. The same applies to the validation set and test set(without instances).
Please refer to [lyft_converter.py](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/tools/dataset_converters/lyft_converter.py) for more details about the structure of `lyft_infos_xxx.pkl`.
We typically need to organize the useful data information with a .pkl or .json file in a specific style, e.g., coco-style for organizing images and their annotations.
We typically need to organize the useful data information with a `.pkl` file in a specific style.
To prepare these files for nuScenes, run the following command:
```bash
...
...
@@ -56,53 +56,54 @@ mmdetection3d
-`nuscenes_database/xxxxx.bin`: point cloud data included in each 3D bounding box of the training dataset
-`nuscenes_infos_train.pkl`: training dataset, a dict contains two keys: `metainfo` and `data_list`.
`metadata` contains the basic information for the dataset itself, such as `CLASSES` and `version`, while `data_list` is a list of dict, each dict (hereinafter referred to as`info`) contains all the detailed information of single sample as follows:
`metainfo` contains the basic information for the dataset itself, such as `categories`, `dataset` and `info_version`, while `data_list` is a list of dict, each dict (hereinafter referred to as`info`) contains all the detailed information of single sample as follows:
- info\['sample_idx'\]: The index of this sample in the whole dataset.
- info\['token'\]: Sample data token.
- info\['timestamp'\]: Timestamp of the sample data.
- info\['lidar_points'\]: A dict contains all the information related to the lidar points.
- info\['lidar_points'\]: A dict containing all the information related to the lidar points.
- info\['lidar_points'\]\['lidar_path'\]: The filename of the lidar point cloud data.
- info\['lidar_points'\]\['num_pts_feats'\]: The feature dimension of point.
- info\['lidar_points'\]\['lidar2ego'\]: The transformation matrix from this lidar sensor to ego vehicle. (4x4 list)
- info\['lidar_points'\]\['ego2global'\]: The transformation matrix from the ego vehicle to global coordinates. (4x4 list)
- info\['lidar_sweeps'\]: A list contains sweeps information (The intermediate lidar frames without annotations)
- info\['lidar_sweeps'\]\[i\]\['lidar_points'\]\['data_path'\]: The lidar data path of i-th sweep.
- info\['lidar_sweeps'\]\[i\]\['lidar_points'\]\['lidar2ego'\]: The transformation matrix from this lidar sensor to ego vehicle. (4x4 list)
- info\['lidar_sweeps'\]\[i\]\['lidar_points'\]\['ego2global'\]: The transformation matrix from the ego vehicle to global coordinates. (4x4 list)
- info\['lidar_sweeps'\]\[i\]\['lidar2sensor'\]: The transformation matrix from the main lidar sensor to the current sensor (for collecting the sweep data) to lidar. (4x4 list)
- info\['lidar_sweeps'\]\[i\]\['lidar2sensor'\]: The transformation matrix from the main lidar sensor to the current sensor (for collecting the sweep data). (4x4 list)
- info\['lidar_sweeps'\]\[i\]\['timestamp'\]: Timestamp of the sweep data.
- info\['lidar_sweeps'\]\[i\]\['sample_data_token'\]: The sweep sample data token.
- info\['images'\]: A dict contains six keys corresponding to each camera: `'CAM_FRONT'`, `'CAM_FRONT_RIGHT'`, `'CAM_FRONT_LEFT'`, `'CAM_BACK'`, `'CAM_BACK_LEFT'`, `'CAM_BACK_RIGHT'`. Each dict contains all data information related to corresponding camera.
- info\['images'\]\['CAM_XXX'\]\['img_path'\]: Filename of image.
- info\['images'\]\['CAM_XXX'\]\['img_path'\]: The filename of the image.
- info\['images'\]\['CAM_XXX'\]\['cam2img'\]: The transformation matrix recording the intrinsic parameters when projecting 3D points to each image plane. (3x3 list)
- info\['images'\]\['CAM_XXX'\]\['sample_data_token'\]: Sample data token of image.
- info\['images'\]\['CAM_XXX'\]\['timestamp'\]: Timestamp of the image.
- info\['images'\]\['CAM_XXX'\]\['cam2ego'\]: The transformation matrix from this camera sensor to ego vehicle. (4x4 list)
- info\['images'\]\['CAM_XXX'\]\['lidar2cam'\]: The transformation matrix from lidar sensor to this camera. (4x4 list)
- info\['instances'\]: It is a list of dict. Each dict contains all annotation information of single instance.
- info\['instances'\]\['bbox_3d'\]: List of 7 numbers representing the 3D bounding box of the instance, in (x, y, z, l, w, h, yaw) order.
- info\['instances'\]\['bbox_label_3d'\]: A int indicate the label of instance and the -1 indicate ignore.
- info\['instances'\]\['velocity'\]: Velocities of 3D bounding boxes (no vertical measurements due to inaccuracy), a list has shape (2.).
- info\['instances'\]\['num_lidar_pts'\]: Number of lidar points included in each 3D bounding box.
- info\['instances'\]\['num_radar_pts'\]: Number of radar points included in each 3D bounding box.
- info\['instances'\]\['bbox_3d_isvalid'\]: Whether each bounding box is valid. In general, we only take the 3D boxes that include at least one lidar or radar point as valid boxes.
- info\['cam_instances'\]: It is a dict contains keys `'CAM_FRONT'`, `'CAM_FRONT_RIGHT'`, `'CAM_FRONT_LEFT'`, `'CAM_BACK'`, `'CAM_BACK_LEFT'`, `'CAM_BACK_RIGHT'`. For vision-based 3D object detection task, we split 3D annotations of the whole scenes according to the camera they belong to.
- info\['cam_instances'\]\['CAM_XXX'\]\['bbox_label'\]: Label of instance.
- info\['cam_instances'\]\['CAM_XXX'\]\['bbox_label_3d'\]: Label of instance.
- info\['cam_instances'\]\['CAM_XXX'\]\['bbox'\]: 2D bounding box annotation (exterior rectangle of the projected 3D box), a list arrange as \[x1, y1, x2, y2\].
- info\['cam_instances'\]\['CAM_XXX'\]\['center_2d'\]: Projected center location on the image, a list has shape (2,), .
- info\['cam_instances'\]\['CAM_XXX'\]\['depth'\]: The depth of projected center.
- info\['cam_instances'\]\['CAM_XXX'\]\['velocity'\]: Velocities of 3D bounding boxes (no vertical measurements due to inaccuracy), a list has shape (2,).
- info\['cam_instances'\]\['CAM_XXX'\]\['attr_label'\]: The attr label of instance. We maintain a default attribute collection and mapping for attribute classification.
- info\['cam_instances'\]\['CAM_XXX'\]\['bbox_3d'\]: List of 7 numbers representing the 3D bounding box of the instance, in (x, y, z, l, h, w, yaw) order.
- info\['instances'\]: It is a list of dict. Each dict contains all annotation information of single instance. For the i-th instance:
- info\['instances'\]\[i\]\['bbox_3d'\]: List of 7 numbers representing the 3D bounding box of the instance, in (x, y, z, l, w, h, yaw) order.
- info\['instances'\]\[i\]\['bbox_label_3d'\]: A int indicate the label of instance and the -1 indicate ignore.
- info\['instances'\]\[i\]\['velocity'\]: Velocities of 3D bounding boxes (no vertical measurements due to inaccuracy), a list has shape (2.).
- info\['instances'\]\[i\]\['num_lidar_pts'\]: Number of lidar points included in each 3D bounding box.
- info\['instances'\]\[i\]\['num_radar_pts'\]: Number of radar points included in each 3D bounding box.
- info\['instances'\]\[i\]\['bbox_3d_isvalid'\]: Whether each bounding box is valid. In general, we only take the 3D boxes that include at least one lidar or radar point as valid boxes.
- info\['cam_instances'\]: It is a dict containing keys `'CAM_FRONT'`, `'CAM_FRONT_RIGHT'`, `'CAM_FRONT_LEFT'`, `'CAM_BACK'`, `'CAM_BACK_LEFT'`, `'CAM_BACK_RIGHT'`. For vision-based 3D object detection task, we split 3D annotations of the whole scenes according to the camera they belong to. For the i-th instance:
- info\['cam_instances'\]\['CAM_XXX'\]\[i\]\['bbox_label'\]: Label of instance.
- info\['cam_instances'\]\['CAM_XXX'\]\[i\]\['bbox_label_3d'\]: Label of instance.
- info\['cam_instances'\]\['CAM_XXX'\]\[i\]\['bbox'\]: 2D bounding box annotation (exterior rectangle of the projected 3D box), a list arrange as \[x1, y1, x2, y2\].
- info\['cam_instances'\]\['CAM_XXX'\]\[i\]\['center_2d'\]: Projected center location on the image, a list has shape (2,), .
- info\['cam_instances'\]\['CAM_XXX'\]\[i\]\['depth'\]: The depth of projected center.
- info\['cam_instances'\]\['CAM_XXX'\]\[i\]\['velocity'\]: Velocities of 3D bounding boxes (no vertical measurements due to inaccuracy), a list has shape (2,).
- info\['cam_instances'\]\['CAM_XXX'\]\[i\]\['attr_label'\]: The attr label of instance. We maintain a default attribute collection and mapping for attribute classification.
- info\['cam_instances'\]\['CAM_XXX'\]\[i\]\['bbox_3d'\]: List of 7 numbers representing the 3D bounding box of the instance, in (x, y, z, l, h, w, yaw) order.
Note:
1. The differences between `bbox_3d` in `instances`and that in `cam_instances`.
Both `bbox_3d`have been converted to MMDet3D coordinate system, but `bboxes_3d` in `instances` is in LiDAR coordinate format and `bboxes_3d` in `cam_instances` is in Camera coordinate format. Mind the difference between them in 3D Box representation ('l, w, h' and 'l, h, w').
1. The differences between `bbox_3d` in `instances` and that in `cam_instances`.
Both `bbox_3d` have been converted to MMDet3D coordinate system, but `bboxes_3d` in `instances` is in LiDAR coordinate format and `bboxes_3d` in `cam_instances` is in Camera coordinate format. Mind the difference between them in 3D Box representation ('l, w, h' and 'l, h, w').
2. Here we only explain the data recorded in the training info files. The same applies to validation and testing set (the pkl of test set does not contains `instances` and `cam_instances`).
2. Here we only explain the data recorded in the training info files. The same applies to validation and testing set (the `.pkl` file of test set does not contains `instances` and `cam_instances`).
The core function to get `nuscenes_infos_xxx.pkl`are[\_fill_trainval_infos](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/tools/dataset_converters/nuscenes_converter.py#L146) and [get_2d_boxes](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/tools/dataset_converters/nuscenes_converter.py#L397), respectively.
The core function to get `nuscenes_infos_xxx.pkl`is[\_fill_trainval_infos](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/tools/dataset_converters/nuscenes_converter.py#L146).
Please refer to [nuscenes_converter.py](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/tools/dataset_converters/nuscenes_converter.py) for more details.
Typically mean intersection over union (mIoU) is used for evaluation on S3DIS. In detail, we first compute IoU for multiple classes and then average them to get mIoU, please refer to [seg_eval.py](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/core/evaluation/seg_eval.py).
Typically mean intersection over union (mIoU) is used for evaluation on S3DIS. In detail, we first compute IoU for multiple classes and then average them to get mIoU, please refer to [seg_eval.py](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/mmdet3d/evaluation/functional/seg_eval.py).
As introduced in section `Export S3DIS data`, S3DIS trains on 5 areas and evaluates on the remaining 1 area. But there are also other area split schemes in different papers.
To enable flexible combination of train-val splits, we use sub-dataset to represent one area, and concatenate them to form a larger training set. An example of training on area 1, 2, 3, 4, 6 and evaluating on area 5 is shown as below:
where we specify the areas used for training/validation by setting `ann_files` and `scene_idxs` with lists that include corresponding paths. The train-val split can be simply modified via changing the `train_area` and `test_area` variables.
For the overall process, please refer to the [README](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/data/scannet/README.md/) page for ScanNet.
For the overall process, please refer to the [README](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/data/scannet/README.md) page for ScanNet.
The directory structure after process should be as below
The directory structure after process should be as below:
```
scannet
...
...
@@ -226,13 +226,13 @@ scannet
-`semantic_mask/xxxxx.bin`: The semantic label for each point, value range: \[1, 40\], i.e. `nyu40id` standard. Note: the `nyu40id` ID will be mapped to train ID in train pipeline `PointSegClassMapping`.
-`posed_images/scenexxxx_xx`: The set of `.jpg` images with `.txt` 4x4 poses and the single `.txt` file with camera intrinsic matrix.
-`scannet_infos_train.pkl`: The train data infos, the detailed info of each scan is as follows:
- info\['lidar_points'\]: A dict contains all information relate to the the lidar points.
- info\['lidar_points'\]\['lidar_path'\]: The filename of `xxx.bin` of lidar points.
- info\['lidar_points'\]: A dict containing all information related to the lidar points.
- info\['lidar_points'\]\['lidar_path'\]: The filename of the lidar point cloud data.
- info\['lidar_points'\]\['num_pts_feats'\]: The feature dimension of point.
- info\['lidar_points'\]\['axis_align_matrix'\]: The transformation matrix to align the axis.
- info\['pts_semantic_mask_path'\]: The filename of `xxx.bin` contains semantic mask annotation.
- info\['pts_instance_mask_path'\]: The filename of `xxx.bin` contains semantic mask annotation.
- info\['instances'\]: A list of dict contains all annotations, each dict contains all annotation information of single instance.
- info\['pts_semantic_mask_path'\]: The filename of the semantic mask annotation.
- info\['pts_instance_mask_path'\]: The filename of the instance mask annotation.
- info\['instances'\]: A list of dict contains all annotations, each dict contains all annotation information of single instance. For the i-th instance:
- info\['instances'\]\[i\]\['bbox_3d'\]: List of 6 numbers representing the axis-aligned 3D bounding box of the instance in depth coordinate system, in (x, y, z, l, w, h) order.
- info\['instances'\]\[i\]\['bbox_label_3d'\]: The label of each 3d bounding boxes.
-`scannet_infos_val.pkl`: The val data infos, which shares the same format as `scannet_infos_train.pkl`.
...
...
@@ -257,8 +257,7 @@ train_pipeline = [
with_mask_3d=True,
with_seg_3d=True),
dict(type='GlobalAlignment',rotation_axis=2),
dict(
type='PointSegClassMapping'),
dict(type='PointSegClassMapping'),
dict(type='PointSample',num_points=40000),
dict(
type='RandomFlip3D',
...
...
@@ -288,6 +287,6 @@ train_pipeline = [
## Metrics
Typically mean Average Precision (mAP) is used for evaluation on ScanNet, e.g. `mAP@0.25` and `mAP@0.5`. In detail, a generic function to compute precision and recall for 3D object detection for multiple classes is called, please refer to [indoor_eval](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/mmdet3d/evaluation/functional/indoor_eval.py).
Typically mean Average Precision (mAP) is used for evaluation on ScanNet, e.g. `mAP@0.25` and `mAP@0.5`. In detail, a generic function to compute precision and recall for 3D object detection for multiple classes is called. Please refer to [indoor_eval](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/mmdet3d/evaluation/functional/indoor_eval.py) for more details.
As introduced in section `Export ScanNet data`, all ground truth 3D bounding box are axis-aligned, i.e. the yaw is zero. So the yaw target of network predicted 3D bounding box is also zero and axis-aligned 3D Non-Maximum Suppression (NMS), which is regardless of rotation, is adopted during post-processing .
The overall process is similar to ScanNet 3D detection task. Please refer to this [section](https://github.com/open-mmlab/mmdetection3d/blob/master/docs/en/datasets/scannet_det.md#dataset-preparation). Only a few differences and additional information about the 3D semantic segmentation data will be listed below.
The overall process is similar to ScanNet 3D detection task. Please refer to this [section](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/docs/en/advanced_guides/datasets/scannet_det.md#dataset-preparation). Only a few differences and additional information about the 3D semantic segmentation data will be listed below.
Typically mean Intersection over Union (mIoU) is used for evaluation on ScanNet. In detail, we first compute IoU for multiple classes and then average them to get mIoU, please refer to [seg_eval](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/core/evaluation/seg_eval.py).
Typically mean Intersection over Union (mIoU) is used for evaluation on ScanNet. In detail, we first compute IoU for multiple classes and then average them to get mIoU, please refer to [seg_eval](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/mmdet3d/evaluation/functional/seg_eval.py).
For the overall process, please refer to the [README](https://github.com/open-mmlab/mmdetection3d/blob/master/data/sunrgbd/README.md/) page for SUN RGB-D.
For the overall process, please refer to the [README](https://github.com/open-mmlab/mmdetection3d/blob/master/data/sunrgbd/README.md) page for SUN RGB-D.
### Download SUN RGB-D data and toolbox
...
...
@@ -151,21 +151,21 @@ sunrgbd
├── sunrgbd_infos_val.pkl
```
-`points/0xxxxx.bin`: The point cloud data after downsample.
-`points/xxxxxx.bin`: The point cloud data after downsample.
-`sunrgbd_infos_train.pkl`: The train data infos, the detailed info of each scene is as follows:
- info\['lidar_points'\]: A dict contains all information relate to the the lidar points.
- info\['lidar_points'\]: A dict containing all information related to the the lidar points.
- info\['lidar_points'\]\['num_pts_feats'\]: The feature dimension of point.
- info\['lidar_points'\]\['lidar_path'\]: The filename of `xxx.bin` of lidar points.
- info\['images'\]: A dict contains all information relate to the image data.
- info\['images'\]\['CAM0'\]\['img_path'\]: The image file name.
- info\['lidar_points'\]\['lidar_path'\]: The filename of the lidar point cloud data.
- info\['images'\]: A dict containing all information relate to the image data.
- info\['images'\]\['CAM0'\]\['img_path'\]: The filename of the image.
- info\['images'\]\['CAM0'\]\['depth2img'\]: Transformation matrix from depth to image with shape (4, 4).
- info\['images'\]\['CAM0'\]\['height'\]: The height of image.
- info\['images'\]\['CAM0'\]\['width'\]: The width of image.
- info\['instances'\]: A list of dict contains all the annotations of this frame. Each dict corresponds to annotations of single instance.
- info\['instances'\]\['bbox_3d'\]: List of 7 numbers representing the 3D bounding box in depth coordinate system.
- info\['instances'\]\['bbox'\]: List of 4 numbers representing the 2D bounding box of the instance, in (x1, y1, x2, y2) order.
- info\['instances'\]\['bbox_label_3d'\]: An int indicates the 3D label of instance and the -1 indicates ignore class.
- info\['instances'\]\['bbox_label'\]: An int indicates the 2D label of instance and the -1 indicates ignore class.
- info\['instances'\]: A list of dict contains all the annotations of this frame. Each dict corresponds to annotations of single instance. For the i-th instance:
- info\['instances'\]\[i\]\['bbox_3d'\]: List of 7 numbers representing the 3D bounding box in depth coordinate system.
- info\['instances'\]\[i\]\['bbox'\]: List of 4 numbers representing the 2D bounding box of the instance, in (x1, y1, x2, y2) order.
- info\['instances'\]\[i\]\['bbox_label_3d'\]: An int indicates the 3D label of instance and the -1 indicates ignore class.
- info\['instances'\]\[i\]\['bbox_label'\]: An int indicates the 2D label of instance and the -1 indicates ignore class.
-`sunrgbd_infos_val.pkl`: The val data infos, which shares the same format as `sunrgbd_infos_train.pkl`.
-`Resize`: resize the input image, `keep_ratio=True` means the ratio of the image is kept unchanged.
-`RandomFlip`: randomly flip the input image.
The image augmentation and normalization functions are implemented in [MMDetection](https://github.com/open-mmlab/mmdetection/tree/master/mmdet/datasets/pipelines).
The image augmentation functions are implemented in [MMDetection](https://github.com/open-mmlab/mmdetection/tree/dev-3.x/mmdet/datasets/transforms).
## Metrics
Same as ScanNet, typically mean Average Precision (mAP) is used for evaluation on SUN RGB-D, e.g. `mAP@0.25` and `mAP@0.5`. In detail, a generic function to compute precision and recall for 3D object detection for multiple classes is called, please refer to [indoor_eval](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/mmdet3d/evaluation/functional/indoor_eval.py).
Same as ScanNet, typically mean Average Precision (mAP) is used for evaluation on SUN RGB-D, e.g. `mAP@0.25` and `mAP@0.5`. In detail, a generic function to compute precision and recall for 3D object detection for multiple classes is called. Please refer to [indoor_eval](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/mmdet3d/evaluation/functional/indoor_eval.py) for more details.
Since SUN RGB-D consists of image data, detection on image data is also feasible. For instance, in ImVoteNet, we first train an image detector, and we also use mAP for evaluation, e.g. `mAP@0.5`. We use the `eval_map` function from [MMDetection](https://github.com/open-mmlab/mmdetection) to calculate mAP.
@@ -101,11 +101,12 @@ Considering there are many similar frames in the original dataset, we can basica
## Evaluation
For evaluation on Waymo, please follow the [instruction](https://github.com/waymo-research/waymo-open-dataset/blob/master/docs/quick_start.md/) to build the binary file `compute_detection_metrics_main` for metrics computation and put it into `mmdet3d/core/evaluation/waymo_utils/`. Basically, you can follow the commands below to install `bazel` and build the file.
For evaluation on Waymo, please follow the [instruction](https://github.com/waymo-research/waymo-open-dataset/blob/master/docs/quick_start.md) to build the binary file `compute_detection_metrics_main` for metrics computation and put it into `mmdet3d/core/evaluation/waymo_utils/`. Basically, you can follow the commands below to install `bazel` and build the file.
# git clone https://github.com/Abyssaledge/waymo-open-dataset-master waymo-od # if you want to use faster multi-thread version.
cd waymo-od
git checkout remotes/origin/master
...
...
@@ -145,7 +146,7 @@ Then you can evaluate your models on Waymo. An example to evaluate PointPillars
An example to test PointPillars on Waymo with 8 GPUs, generate the bin files and make a submission to the leaderboard.
`submission_prefix` should be set in `test_evaluator` of configuration before you run the test command if you want to generate the bin files and make a submission to the leaderboard..
`submission_prefix` should be set in `test_evaluator` of configuration before you run the test command if you want to generate the bin files and make a submission to the leaderboard..
After generating the bin file, you can simply build the binary file `create_submission` and use them to create a submission file by following the [instruction](https://github.com/waymo-research/waymo-open-dataset/blob/master/docs/quick_start.md/). Basically, here are some example commands.
If you are experienced with PyTorch and have already installed it, just skip this part and jump to the [next section](#installation). Otherwise, you can follow these steps for the preparation.
...
...
@@ -48,7 +49,7 @@ Assuming that you already have CUDA 11.0 installed, here is a full script for qu
Otherwise, you should refer to the step-by-step installation instructions in the next section.
1. The git commit id will be written to the version number with step d, e.g. 0.6.0+2e7045c. The version will also be saved in trained models.
It is recommended that you run step d each time you pull some updates from github. If C++/CUDA codes are modified, then this step is compulsory.
1. The git commit id will be written to the version number with step 3, e.g. `0.6.0+2e7045c`. The version will also be saved in trained models.
It is recommended that you run step 3 each time you pull some updates from github. If C++/CUDA codes are modified, then this step is compulsory.
> Important: Be sure to remove the `./build` folder if you reinstall mmdet with a different CUDA/PyTorch version.
> Important: Be sure to remove the `./build` folder if you reinstall mmdet3d with a different CUDA/PyTorch version.
```shell
pip uninstall mmdet3d
...
...
@@ -117,20 +118,20 @@ Note:
4. Some dependencies are optional. Simply running `pip install -v -e .` will only install the minimum runtime requirements. To use optional dependencies like `albumentations` and `imagecorruptions` either install them manually with `pip install -r requirements/optional.txt` or specify desired extras when calling `pip` (e.g. `pip install -v -e .[optional]`). Valid keys for the extras field are: `all`, `tests`, `build`, and `optional`.
We have supported spconv2.0. If the user has installed spconv2.0, the code will use spconv2.0 first, which will take up less GPU memory than using the default mmcv spconv. Users can use the following commands to install spconv2.0:
We have supported `spconv2.0`. If the user has installed `spconv2.0`, the code will use `spconv2.0` first, which will take up less GPU memory than using the default `mmcv spconv`. Users can use the following commands to install `spconv2.0`:
```bash
pip install cumm-cuxxx
pip install spconv-cuxxx
```
Where xxx is the CUDA version in the environment.
Where `xxx` is the CUDA version in the environment.
For example, using CUDA 10.2, the command will be `pip install cumm-cu102 && pip install spconv-cu102`.
Supported CUDA versions include 10.2, 11.1, 11.3, and 11.4. Users can also install it by building from the source. For more details please refer to [spconv v2.x](https://github.com/traveller59/spconv).
We also support Minkowski Engine as a sparse convolution backend. If necessary please follow original [installation guide](https://github.com/NVIDIA/MinkowskiEngine#installation) or use `pip`:
We also support `Minkowski Engine` as a sparse convolution backend. If necessary please follow original [installation guide](https://github.com/NVIDIA/MinkowskiEngine#installation) or use `pip` to install it:
If you want to input a `ply` file, you can use the following function and convert it to `bin` format. Then you can use the converted `bin` file to generate demo.
If you want to input a `.ply` file, you can use the following function and convert it to `.bin` format. Then you can use the converted `.bin` file to run demo.
Note that you need to install `pandas` and `plyfile` before using this script. This function can also be used for data preprocessing for training `ply data`.
```python
...
...
@@ -181,7 +182,7 @@ Examples:
convert_ply('./test.ply','./test.bin')
```
If you have point clouds in other format (`off`, `obj`, etc.), you can use `trimesh` to convert them into `ply`.
If you have point clouds in other format (`.off`, `.obj`, etc.), you can use `trimesh` to convert them into `.ply`.
```python
importtrimesh
...
...
@@ -197,7 +198,7 @@ Examples:
to_ply('./test.obj','./test.ply','obj')
```
More demos about single/multi-modality and indoor/outdoor 3D detection can be found in [demo](demo.md).
More demos about single/multi-modality and indoor/outdoor 3D detection can be found in [demo](user_guides/inference.md).
## Customize Installation
...
...
@@ -216,9 +217,9 @@ Installing CUDA runtime libraries is enough if you follow our best practices, be
### Install MMEngine without MIM
To install MMEngine with pip instead of MIM, please follow [MMEngine installation guides](https://mmcv.readthedocs.io/en/latest/get_started/installation.html).
To install MMEngine with pip instead of MIM, please follow [MMEngine installation guides](https://mmengine.readthedocs.io/en/latest/get_started/installation.html).
For example, you can install MMEngine by the following command.
For example, you can install MMEngine by the following command:
```shell
pip install mmengine
...
...
@@ -228,9 +229,9 @@ pip install mmengine
MMCV contains C++ and CUDA extensions, thus depending on PyTorch in a complex way. MIM solves such dependencies automatically and makes the installation easier. However, it is not a must.
To install MMCV with pip instead of MIM, please follow [MMCV installation guides](https://mmcv.readthedocs.io/en/latest/get_started/installation.html). This requires manually specifying a find-url based on PyTorch version and its CUDA version.
To install MMCV with pip instead of MIM, please follow [MMCV installation guides](https://mmcv.readthedocs.io/en/2.x/get_started/installation.html). This requires manually specifying a find-url based on PyTorch version and its CUDA version.
For example, the following command install mmcv built for PyTorch 1.10.x and CUDA 11.3.
For example, the following command install MMCV built for PyTorch 1.10.x and CUDA 11.3:
@@ -104,6 +104,14 @@ Please refer to [MonoFlex](https://github.com/open-mmlab/mmdetection3d/tree/v1.0
Please refer to [SA-SSD](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/sassd) for details. We provide SA-SSD baselines on the KITTI dataset.
### FCAF3D
Please refer to [FCAF3D](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/fcaf3d) for details. We provide FCAF3D baselines on the ScanNet, S3DIS, and SUN RGB-D datasets.
### PV-RCNN
Please refer to [PV-RCNN](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/configs/pv_rcnn) for details. We provide PV-RCNN baselines on the KITTI dataset.
### Mixed Precision (FP16) Training
Please refer to [Mixed Precision (FP16) Training on PointPillars](https://github.com/open-mmlab/mmdetection3d/tree/v1.0.0.dev0/configs/pointpillars/hv_pointpillars_fpn_sbn-all_fp16_2x8_2x_nus-3d.py) for details.
We list some potential troubles encountered by users and developers, along with their corresponding solutions. Feel free to enrich the list if you find any frequent issues and contribute your solutions to solve them. If you have any trouble with environment configuration, model training, etc, please create an issue using the [provided templates](https://github.com/open-mmlab/mmdetection3d/blob/master/.github/ISSUE_TEMPLATE/error-report.md/) and fill in all required information in the template.
We list some potential troubles encountered by users and developers, along with their corresponding solutions. Feel free to enrich the list if you find any frequent issues and contribute your solutions to solve them. If you have any trouble with environment configuration, model training, etc, please create an issue using the [provided templates](https://github.com/open-mmlab/mmdetection3d/blob/master/.github/ISSUE_TEMPLATE/error-report.md) and fill in all required information in the template.
## MMCV/MMDet/MMDet3D Installation
## MMEngine/MMCV/MMDet/MMDet3D Installation
- Compatibility issue between MMEngine, MMCV, MMDetection and MMDetection3D; "ConvWS is already registered in conv layer"; "AssertionError: MMCV==xxx is used but incompatible. Please install mmcv>=xxx, \<=xxx."
- The required versions of MMEngine, MMCV and MMDetection for different versions of MMDetection3D are as below. Please install the correct version of MMEngine, MMCV and MMDetection to avoid installation issues.
| MMDetection3D version | MMEngine version | MMCV version | MMDetection version |
**Note:** If you want to install mmdet3d-v1.0.0x, the compatible MMDetection, MMSegmentation and MMCV versions table can be found at [here](https://mmdetection3d.readthedocs.io/en/latest/faq.html#mmcv-mmdet-mmdet3d-installation). Please choose the correct version of MMCV to avoid installation issues.
**Note:** If you want to install mmdet3d-v1.0.0rcx, the compatible MMDetection, MMSegmentation and MMCV versions table can be found at [here](https://mmdetection3d.readthedocs.io/en/latest/faq.html#mmcv-mmdet-mmdet3d-installation). Please choose the correct version of MMCV, MMDetection and MMSegmentation to avoid installation issues.
- If you faced the error shown below when importing open3d:
MMDetection3D and other OpenMMLab repositories use [MMEngine's config system](https://mmengine.readthedocs.io/en/latest/tutorials/config.md). It has a modular and inheritance design, which is convenient to conduct various experiments.
MMDetection3D and other OpenMMLab repositories use [MMEngine's config system](https://mmengine.readthedocs.io/en/latest/tutorials/config.html). It has a modular and inheritance design, which is convenient to conduct various experiments.
If you wish to inspect the config file, you may run `python tools/misc/print_config.py /PATH/TO/CONFIG` to see the complete config.
## Config File Content
MMDetection3D uses a modular design, all modules with different functions can be configured through the config. Taking PointPillars as an example, we will introduce each field in the config according to different function modules:
MMDetection3D uses a modular design, all modules with different functions can be configured through the config. Taking PointPillars as an example, we will introduce each field in the config according to different function modules.
@@ -431,7 +431,7 @@ default_scope = 'mmdet3d' # The default registry scope to find modules. Refer t
env_cfg=dict(
cudnn_benchmark=False,# Whether to enable cudnn benchmark
mp_cfg=dict(mp_start_method='fork',opencv_num_threads=0),# Use fork to start multi-processing threads. 'fork' usually faster than 'spawn' but maybe unsafe. See discussion in https://github.com/pytorch/pytorch/issues/1355
mp_cfg=dict(mp_start_method='fork',opencv_num_threads=0),# Use fork to start multi-processing threads. 'fork' usually faster than 'spawn' but maybe unsafe. See discussion in https://github.com/pytorch/pytorch/issues/1355
dist_cfg=dict(backend='nccl'))# Distribution configs
@@ -7,6 +7,7 @@ MMDetection3D uses three different coordinate systems. The existence of differen
Despite the variety of datasets and equipment, by summarizing the line of works on 3D object detection we can roughly categorize coordinate systems into three:
- Camera coordinate system -- the coordinate system of most cameras, in which the positive direction of the y-axis points to the ground, the positive direction of the x-axis points to the right, and the positive direction of the z-axis points to the front.
```
up z front
| ^
...
...
@@ -22,7 +23,9 @@ Despite the variety of datasets and equipment, by summarizing the line of works
v
y down
```
- LiDAR coordinate system -- the coordinate system of many LiDARs, in which the negative direction of the z-axis points to the ground, the positive direction of the x-axis points to the front, and the positive direction of the y-axis points to the left.
```
z up x front
^ ^
...
...
@@ -32,7 +35,9 @@ Despite the variety of datasets and equipment, by summarizing the line of works
|/
y left <------ 0 ------ right
```
- Depth coordinate system -- the coordinate system used by VoteNet, H3DNet, etc., in which the negative direction of the z-axis points to the ground, the positive direction of the x-axis points to the right, and the positive direction of the y-axis points to the front.
