News: We released the codebase v0.9.0.
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 will be released soon!
Documentation: https://mmdetection3d.readthedocs.io/
The master branch works with PyTorch 1.3 to 1.6.
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.
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Support multi-modality/single-modality detectors out of box
It directly supports multi-modality/single-modality detectors including MVXNet, VoteNet, PointPillars, etc.
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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.
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Natural integration with 2D detection
All the about 40+ methods, 300+ models, and modules supported in MMDetection can be trained or used in this codebase.
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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.
This project is released under the Apache 2.0 license.
v0.9.0 was released in 31/12/2020. Please refer to changelog.md for details and release history.
Supported methods and backbones are shown in the below table. Results and models are available in the model zoo.
| ResNet | ResNeXt | SENet | PointNet++ | HRNet | RegNetX | Res2Net | |
|---|---|---|---|---|---|---|---|
| SECOND | ☐ | ☐ | ☐ | ✗ | ☐ | ✓ | ☐ |
| PointPillars | ☐ | ☐ | ☐ | ✗ | ☐ | ✓ | ☐ |
| FreeAnchor | ☐ | ☐ | ☐ | ✗ | ☐ | ✓ | ☐ |
| VoteNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
| H3DNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
| 3DSSD | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
| Part-A2 | ☐ | ☐ | ☐ | ✗ | ☐ | ✓ | ☐ |
| MVXNet | ☐ | ☐ | ☐ | ✗ | ☐ | ✓ | ☐ |
| CenterPoint | ☐ | ☐ | ☐ | ✗ | ☐ | ✓ | ☐ |
| SSN | ☐ | ☐ | ☐ | ✗ | ☐ | ✓ | ☐ |
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 getting_started.md for installation.
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.
We appreciate all contributions to improve MMDetection3D. Please refer to CONTRIBUTING.md for the contributing guideline.
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.