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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-A^2 net] and [PV-RCNN].

Overview

Changelog

[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] NEW: 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 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
PointRCNN ~3 hours 78.70 54.41 72.11 model-16M
PointRCNN-IoU ~3 hours 78.75 58.32 71.34 model-16M
Part-A^2-Free ~3.8 hours 78.72 65.99 74.29 model-226M
Part-A^2-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
CaDDN ~15 hours 21.38 13.02 9.76 model-774M

NuScenes 3D Object Detection Baselines

All models are trained with 8 GTX 1080Ti GPUs and are available for download.

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

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 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).

Vec_L1 Vec_L2 Ped_L1 Ped_L2 Cyc_L1 Cyc_L2
SECOND 68.03/67.44 59.57/59.04 61.14/50.33 53.00/43.56 54.66/53.31 52.67/51.37
Part-A^2-Anchor 71.82/71.29 64.33/63.82 63.15/54.96 54.24/47.11 65.23/63.92 62.61/61.35
PV-RCNN 74.06/73.38 64.99/64.38 62.66/52.68 53.80/45.14 63.32/61.71 60.72/59.18

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.

Other datasets

More datasets are on the way.

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.