MMDetection3D 代码注释

• 调试过程可参考：https://zhuanlan.zhihu.com/p/444441266
• 注：由于MMDetection3D依赖与MMDetection和MMCV，因此代码注释不全，具体参考流程图利用pycharm调试分析即可

News: We released the codebase v0.17.2.

In addition, we have preliminarily supported several new models on the v1.0.0.dev0 branch, including DGCNN, SMOKE and PGD.

Note: We are going through large refactoring to provide simpler and more unified usage of many modules. Thus, few features will be added to the master branch in the following months.

The compatibilities of models are broken due to the unification and simplification of coordinate systems. For now, most models are benchmarked with similar performance, though few models are still being benchmarked.

You can start experiments with v1.0.0.dev0 if you are interested. Please note that our new features will only be supported in v1.0.0 branch afterward.

In the nuScenes 3D detection challenge of the 5th AI Driving Olympics in NeurIPS 2020, we obtained the best PKL award and the second runner-up by multi-modality entry, and the best vision-only results.

Code and models for the best vision-only method, FCOS3D, have been released. Please stay tuned for MoCa.

Documentation: https://mmdetection3d.readthedocs.io/

Introduction

English | 简体中文

The master branch works with PyTorch 1.3+.

MMDetection3D is an open source object detection toolbox based on PyTorch, towards the next-generation platform for general 3D detection. It is a part of the OpenMMLab project developed by MMLab.

Major features

• Support multi-modality/single-modality detectors out of box

  It directly supports multi-modality/single-modality detectors including MVXNet, VoteNet, PointPillars, etc.

• Support indoor/outdoor 3D detection out of box

  It directly supports popular indoor and outdoor 3D detection datasets, including ScanNet, SUNRGB-D, Waymo, nuScenes, Lyft, and KITTI. For nuScenes dataset, we also support nuImages dataset.

• Natural integration with 2D detection

  All the about 300+ models, methods of 40+ papers, and modules supported in MMDetection can be trained or used in this codebase.

• High efficiency

  It trains faster than other codebases. The main results are as below. Details can be found in benchmark.md. We compare the number of samples trained per second (the higher, the better). The models that are not supported by other codebases are marked by ×.

  Methods	MMDetection3D	OpenPCDet	votenet	Det3D
  VoteNet	358	×	77	×
  PointPillars-car	141	×	×	140
  PointPillars-3class	107	44	×	×
  SECOND	40	30	×	×
  Part-A2	17	14	×	×

Like MMDetection and MMCV, MMDetection3D can also be used as a library to support different projects on top of it.

License

This project is released under the Apache 2.0 license.

Changelog

v0.17.2 was released in 1/11/2021. Please refer to changelog.md for details and release history.

For branch v1.0.0.dev0, please refer to changelog_v1.0.md for our latest features and more details.

Benchmark and model zoo

Supported methods and backbones are shown in the below table. Results and models are available in the model zoo.

Support backbones:

• PointNet (CVPR'2017)
• PointNet++ (NeurIPS'2017)
• RegNet (CVPR'2020)

Support methods

• SECOND (Sensor'2018)
• PointPillars (CVPR'2019)
• FreeAnchor (NeurIPS'2019)
• VoteNet (ICCV'2019)
• H3DNet (ECCV'2020)
• 3DSSD (CVPR'2020)
• Part-A2 (TPAMI'2020)
• MVXNet (ICRA'2019)
• CenterPoint (CVPR'2021)
• SSN (ECCV'2020)
• ImVoteNet (CVPR'2020)
• FCOS3D (ICCVW'2021)
• PointNet++ (NeurIPS'2017)
• Group-Free-3D (ICCV'2021)
• ImVoxelNet (Arxiv'2021)
• PAConv (CVPR'2021)

Other features

• Dynamic Voxelization

Note: All the about 300+ models, methods of 40+ papers in 2D detection supported by MMDetection can be trained or used in this codebase.

Installation

Please refer to getting_started.md for installation.

Get Started

Please see getting_started.md for the basic usage of MMDetection3D. We provide guidance for quick run with existing dataset and with customized dataset for beginners. There are also tutorials for learning configuration systems, adding new dataset, designing data pipeline, customizing models, customizing runtime settings and Waymo dataset.

Please refer to FAQ for frequently asked questions. When updating the version of MMDetection3D, please also check the compatibility doc to be aware of the BC-breaking updates introduced in each version.

Citation

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

@misc{mmdet3d2020,
    title={{MMDetection3D: OpenMMLab} next-generation platform for general {3D} object detection},
    author={MMDetection3D Contributors},
    howpublished = {\url{https://github.com/open-mmlab/mmdetection3d}},
    year={2020}
}

Contributing

We appreciate all contributions to improve MMDetection3D. Please refer to CONTRIBUTING.md for the contributing guideline.

Acknowledgement

MMDetection3D is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors as well as users who give valuable feedbacks. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new 3D detectors.

Projects in OpenMMLab

• MMCV: OpenMMLab foundational library for computer vision.
• MIM: MIM Installs OpenMMLab Packages.
• MMClassification: OpenMMLab image classification toolbox and benchmark.
• MMDetection: OpenMMLab detection toolbox and benchmark.
• MMDetection3D: OpenMMLab next-generation platform for general 3D object detection.
• MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
• MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
• MMTracking: OpenMMLab video perception toolbox and benchmark.
• MMPose: OpenMMLab pose estimation toolbox and benchmark.
• MMEditing: OpenMMLab image and video editing toolbox.
• MMOCR: OpenMMLab text detection, recognition and understanding toolbox.
• MMGeneration: OpenMMLab image and video generative models toolbox.