In the recent 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.

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 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.

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:

  • [x] PointNet (CVPR'2017)
  • [x] PointNet++ (NeurIPS'2017)
  • [x] RegNet (CVPR'2020)
ResNet ResNeXt SENet PointNet++ HRNet RegNetX Res2Net

Other features

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


Please refer to for installation.

Get Started

Please see 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.


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

    title={{MMDetection3D: OpenMMLab} next-generation platform for general {3D} object detection},
    author={MMDetection3D Contributors},
    howpublished = {\url{}},