`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)
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@@ -19,6 +21,11 @@ It is also the official code release of [`[PointRCNN]`](https://arxiv.org/abs/18
## Changelog
[2021-01-05] **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.
* 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).
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### 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 |
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@@ -129,7 +136,7 @@ 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).
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)):
