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OpenPCDet

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], [Part-A2-Net], [PV-RCNN], [Voxel R-CNN], [PV-RCNN++] and [MPPNet].

Highlights:

  • OpenPCDet has been updated to v0.6.0 (Sep. 2022).
  • The codes of PV-RCNN++ has been supported.
  • The codes of MPPNet has been supported.
  • The multi-modal 3D detection approaches on Nuscenes have been supported.

Overview

Changelog

[2023-06-30] NEW: Added support for DSVT, which achieves state-of-the-art performance on large-scale Waymo Open Dataset with real-time inference speed (27HZ with TensorRT).

[2023-05-13] NEW: Added support for the multi-modal 3D object detection models on Nuscenes dataset.

  • Support multi-modal Nuscenes detection (See the GETTING_STARTED.md to process data).
  • Support TransFusion-Lidar head, which ahcieves 69.43% NDS on Nuscenes validation dataset.
  • Support BEVFusion, which fuses multi-modal information on BEV space and reaches 70.98% NDS on Nuscenes validation dataset. (see the guideline on how to train/test with BEVFusion).

[2023-04-02] Added support for VoxelNeXt on Nuscenes, Waymo, and Argoverse2 datasets. It is a fully sparse 3D object detection network, which is a clean sparse CNNs network and predicts 3D objects directly upon voxels.

[2022-09-02] NEW: Update OpenPCDet to v0.6.0:

  • Official code release of MPPNet for temporal 3D object detection, which supports long-term multi-frame 3D object detection and ranks 1st place on 3D detection learderboard of Waymo Open Dataset on Sept. 2th, 2022. For validation dataset, MPPNet achieves 74.96%, 75.06% and 74.52% for vehicle, pedestrian and cyclist classes in terms of mAPH@Level_2. (see the guideline on how to train/test with MPPNet).
  • Support multi-frame training/testing on Waymo Open Dataset (see the change log for more details on how to process data).
  • Support to save changing training details (e.g., loss, iter, epoch) to file (previous tqdm progress bar is still supported by using --use_tqdm_to_record). Please use pip install gpustat if you also want to log the GPU related information.
  • Support to save latest model every 5 mintues, so you can restore the model training from latest status instead of previous epoch.

[2022-08-22] Added support for custom dataset tutorial and template

[2022-07-05] Added support for the 3D object detection backbone network Focals Conv.

[2022-02-12] Added support for using docker. Please refer to the guidance in ./docker.

[2022-02-07] Added support for Centerpoint models on Nuscenes Dataset.

[2022-01-14] Added support for dynamic pillar voxelization, following the implementation proposed in H^23D R-CNN with unique operation and torch_scatter package.

[2022-01-05] NEW: Update OpenPCDet to v0.5.2:

  • The code of PV-RCNN++ 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.
  • Support Lyft dataset, see the pull request here.

[2021-12-09] NEW: Update OpenPCDet to v0.5.1:

  • Add PointPillar related baseline configs/results on Waymo Open Dataset.
  • Support Pandaset dataloader, see the pull request here.
  • Support a set of new augmentations, see the pull request here.

[2021-12-01] NEW: OpenPCDet v0.5.0 is released with the following features:

  • Improve the performance of all models on Waymo Open Dataset. Note that you need to re-prepare the training/validation data and ground-truth database of Waymo Open Dataset (see GETTING_STARTED.md).
  • Support anchor-free CenterHead, add configs of CenterPoint and PV-RCNN with CenterHead.
  • Support lastest PyTorch 1.1~1.10 and spconv 1.0~2.x, where spconv 2.x should be easy to install with pip and faster than previous version (see the official update of spconv here).
  • Support config USE_SHARED_MEMORY to use shared memory to potentially speed up the training process in case you suffer from an IO problem.
  • Support better and faster visualization script, and you need to install Open3D firstly.

[2021-06-08] Added support for the voxel-based 3D object detection model Voxel R-CNN.

[2021-05-14] Added support for the monocular 3D object detection model CaDDN.

[2020-11-27] Bugfixed: Please re-prepare the validation infos of Waymo dataset (version 1.2) if you would like to use our provided Waymo evaluation tool (see PR). Note that you do not need to re-prepare the training data and ground-truth database.

[2020-11-10] The Waymo Open Dataset has been supported with state-of-the-art results. Currently we provide the configs and results of SECOND, PartA2 and PV-RCNN on the Waymo Open Dataset, and more models could be easily supported by modifying their dataset configs.

[2020-08-10] Bugfixed: The provided NuScenes models have been updated to fix the loading bugs. Please redownload it if you need to use the pretrained NuScenes models.

[2020-07-30] OpenPCDet v0.3.0 is released with the following features:

[2020-07-17] Add simple visualization codes and a quick demo to test with custom data.

[2020-06-24] OpenPCDet v0.2.0 is released with pretty new structures to support more models and datasets.

[2020-03-16] OpenPCDet v0.1.0 is released.

Introduction

What does OpenPCDet toolbox do?

Note that we have upgrated PCDet from v0.1 to v0.2 with pretty new structures to support various datasets and models.

OpenPCDet is a general PyTorch-based codebase for 3D object detection from point cloud. It currently supports multiple state-of-the-art 3D object detection methods with highly refactored codes for both one-stage and two-stage 3D detection frameworks.

Based on OpenPCDet toolbox, we win the Waymo Open Dataset challenge in 3D Detection, 3D Tracking, Domain Adaptation three tracks among all LiDAR-only methods, and the Waymo related models will be released to OpenPCDet soon.

We are actively updating this repo currently, and more datasets and models will be supported soon. Contributions are also welcomed.

OpenPCDet design pattern

  • Data-Model separation with unified point cloud coordinate for easily extending to custom datasets:

  • Unified 3D box definition: (x, y, z, dx, dy, dz, heading).

  • Flexible and clear model structure to easily support various 3D detection models:

  • Support various models within one framework as:

Currently Supported Features

  • Support both one-stage and two-stage 3D object detection frameworks
  • Support distributed training & testing with multiple GPUs and multiple machines
  • Support multiple heads on different scales to detect different classes
  • Support stacked version set abstraction to encode various number of points in different scenes
  • Support Adaptive Training Sample Selection (ATSS) for target assignment
  • Support RoI-aware point cloud pooling & RoI-grid point cloud pooling
  • Support GPU version 3D IoU calculation and rotated NMS

Model Zoo

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 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
PointPillar ~1.2 hours 77.28 52.29 62.68 model-18M
SECOND ~1.7 hours 78.62 52.98 67.15 model-20M
SECOND-IoU - 79.09 55.74 71.31 model-46M
PointRCNN ~3 hours 78.70 54.41 72.11 model-16M
PointRCNN-IoU ~3 hours 78.75 58.32 71.34 model-16M
Part-A2-Free ~3.8 hours 78.72 65.99 74.29 model-226M
Part-A2-Anchor ~4.3 hours 79.40 60.05 69.90 model-244M
PV-RCNN ~5 hours 83.61 57.90 70.47 model-50M
Voxel R-CNN (Car) ~2.2 hours 84.54 - - model-28M
Focals Conv - F ~4 hours 85.66 - - model-30M
CaDDN (Mono) ~15 hours 21.38 13.02 9.76 model-774M

Waymo Open Dataset Baselines

We provide the setting of DATA_CONFIG.SAMPLED_INTERVAL 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 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
SECOND 70.96/70.34 62.58/62.02 65.23/54.24 57.22/47.49 57.13/55.62 54.97/53.53
PointPillar 70.43/69.83 62.18/61.64 66.21/46.32 58.18/40.64 55.26/51.75 53.18/49.80
CenterPoint-Pillar 70.50/69.96 62.18/61.69 73.11/61.97 65.06/55.00 65.44/63.85 62.98/61.46
CenterPoint-Dynamic-Pillar 70.46/69.93 62.06/61.58 73.92/63.35 65.91/56.33 66.24/64.69 63.73/62.24
CenterPoint 71.33/70.76 63.16/62.65 72.09/65.49 64.27/58.23 68.68/67.39 66.11/64.87
CenterPoint (ResNet) 72.76/72.23 64.91/64.42 74.19/67.96 66.03/60.34 71.04/69.79 68.49/67.28
Part-A2-Anchor 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) 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) 75.95/75.43 68.02/67.54 75.94/69.40 67.66/61.62 70.18/68.98 67.73/66.57
Voxel R-CNN (CenterHead)-Dynamic-Voxel 76.13/75.66 68.18/67.74 78.20/71.98 69.29/63.59 70.75/69.68 68.25/67.21
PV-RCNN++ 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) 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++):

Performance@(train with 100% Data) Vec_L1 Vec_L2 Ped_L1 Ped_L2 Cyc_L1 Cyc_L2
SECOND 72.27/71.69 63.85/63.33 68.70/58.18 60.72/51.31 60.62/59.28 58.34/57.05
CenterPoint-Pillar 73.37/72.86 65.09/64.62 75.35/65.11 67.61/58.25 67.76/66.22 65.25/63.77
Part-A2-Anchor 77.05/76.51 68.47/67.97 75.24/66.87 66.18/58.62 68.60/67.36 66.13/64.93
VoxelNeXt-2D 77.94/77.47 69.68/69.25 80.24/73.47 72.23/65.88 73.33/72.20 70.66/69.56
VoxelNeXt 78.16/77.70 69.86/69.42 81.47/76.30 73.48/68.63 76.06/74.90 73.29/72.18
PV-RCNN (CenterHead) 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++ 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) 79.25/78.78 70.61/70.18 81.83/76.28 73.17/68.00 73.72/72.66 71.21/70.19
DSVT-Pillar 79.44/78.97 71.24/70.81 83.00/77.22 75.45/69.95 76.70/75.70 73.83/72.86
DSVT-Voxel 79.77/79.31 71.67/71.25 83.75/78.92 76.21/71.57 77.57/76.58 74.70/73.73
PV-RCNN++ (ResNet, 2 frames) 80.17/79.70 72.14/71.70 83.48/80.42 75.54/72.61 74.63/73.75 72.35/71.50
MPPNet (4 frames) 81.54/81.06 74.07/73.61 84.56/81.94 77.20/74.67 77.15/76.50 75.01/74.38
MPPNet (16 frames) 82.74/82.28 75.41/74.96 84.69/82.25 77.43/75.06 77.28/76.66 75.13/74.52

We could not provide the above pretrained models due to Waymo Dataset License Agreement, but you could easily achieve similar performance by training with the default configs.

NuScenes 3D Object Detection Baselines

All models are trained with 8 GPUs and are available for download. For training BEVFusion, please refer to the guideline.

mATE mASE mAOE mAVE mAAE mAP NDS download
PointPillar-MultiHead 33.87 26.00 32.07 28.74 20.15 44.63 58.23 model-23M
SECOND-MultiHead (CBGS) 31.15 25.51 26.64 26.26 20.46 50.59 62.29 model-35M
CenterPoint-PointPillar 31.13 26.04 42.92 23.90 19.14 50.03 60.70 model-23M
CenterPoint (voxel_size=0.1) 30.11 25.55 38.28 21.94 18.87 56.03 64.54 model-34M
CenterPoint (voxel_size=0.075) 28.80 25.43 37.27 21.55 18.24 59.22 66.48 model-34M
VoxelNeXt (voxel_size=0.075) 30.11 25.23 40.57 21.69 18.56 60.53 66.65 model-31M
TransFusion-L* 27.96 25.37 29.35 27.31 18.55 64.58 69.43 model-32M
BEVFusion 28.03 25.43 30.19 26.76 18.48 67.75 70.98 model-157M

*: Use the fade strategy, which disables data augmentations in the last several epochs during training.

ONCE 3D Object Detection Baselines

All models are trained with 8 GPUs.

Vehicle Pedestrian Cyclist mAP
PointRCNN 52.09 4.28 29.84 28.74
PointPillar 68.57 17.63 46.81 44.34
SECOND 71.19 26.44 58.04 51.89
PV-RCNN 77.77 23.50 59.37 53.55
CenterPoint 78.02 49.74 67.22 64.99

Argoverse2 3D Object Detection Baselines

All models are trained with 4 GPUs.

mAP download
VoxelNeXt 30.5 model-32M

Other datasets

Welcome to support other datasets by submitting pull request.

Installation

Please refer to INSTALL.md for the installation of OpenPCDet.

Quick Demo

Please refer to DEMO.md for a quick demo to test with a pretrained model and visualize the predicted results on your custom data or the original KITTI data.

Getting Started

Please refer to GETTING_STARTED.md to learn more usage about this project.

License

OpenPCDet is released under the Apache 2.0 license.

Acknowledgement

OpenPCDet is an open source project for LiDAR-based 3D scene perception that supports multiple LiDAR-based perception models as shown above. Some parts of PCDet are learned from the official released codes of the above supported methods. We would like to thank for their proposed methods and the official implementation.

We hope that this repo could serve as a strong and flexible codebase to benefit the research community by speeding up the process of reimplementing previous works and/or developing new methods.

Citation

If you find this project useful in your research, please consider cite:

@misc{openpcdet2020,
    title={OpenPCDet: An Open-source Toolbox for 3D Object Detection from Point Clouds},
    author={OpenPCDet Development Team},
    howpublished = {\url{https://github.com/open-mmlab/OpenPCDet}},
    year={2020}
}

Contribution

Welcome to be a member of the OpenPCDet development team by contributing to this repo, and feel free to contact us for any potential contributions.