MMDetection3D is an open source object detection toolbox based on PyTorch, towards the next-generation platform for general 3D detection. This is the OneDL fork maintained by the VBTI development team, reviving MMLabs code to work with newer PyTorch versions and fixing bugs.
The main branch works with PyTorch 2.0+.
Major features
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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 300+ models, methods of 40+ papers, 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 OneDL-MMDetection and OneDL-MMCV, MMDetection3D can also be used as a library to support different projects on top of it.
In version 1.4, MMDetecion3D refactors the Waymo dataset and accelerates the preprocessing, training/testing setup, and evaluation of Waymo dataset. We also extends the support for camera-based, such as Monocular and BEV, 3D object detection models on Waymo. A detailed description of the Waymo data information is provided here.
Besides, in version 1.4, MMDetection3D provides Waymo-mini to help community users get started with Waymo and use it for quick iterative development.
v1.5.0 was released in 17/4/2026:
- Update to join OneDL ecosystem.
- Support python 3.12 and torch 2.10.
- Removed lyft-dataset-sdk and nuscene-devkit dependencies (for now) because of incompatibilities.
v1.4.0 was released in 8/1/2024：
- Support the training of DSVT in
projects - Support Nerf-Det in
projects - Refactor Waymo dataset
v1.3.0 was released in 18/10/2023:
- Support CENet in
projects - Enhance demos with new 3D inferencers
v1.2.0 was released in 4/7/2023
- Support New Config Type in
mmdet3d/configs - Support the inference of DSVT in
projects - Support downloading datasets from OpenDataLab using
mim
v1.1.1 was released in 30/5/2023:
- Support TPVFormer in
projects - Support the training of BEVFusion in
projects - Support lidar-based 3D semantic segmentation benchmark
Please refer to Installation for installation instructions.
For detailed user guides and advanced guides, please refer to our documentation:
User Guides
Advanced Guides
Results and models are available in the model zoo.
| Backbones | Heads | Features |
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| LiDAR-based 3D Object Detection | Camera-based 3D Object Detection | Multi-modal 3D Object Detection | 3D Semantic Segmentation |
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| ResNet | VoVNet | Swin-T | PointNet++ | SECOND | DGCNN | RegNetX | DLA | MinkResNet | Cylinder3D | MinkUNet | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| SECOND | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| PointPillars | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |
| FreeAnchor | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |
| VoteNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| H3DNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| 3DSSD | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Part-A2 | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| MVXNet | ✓ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| CenterPoint | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| SSN | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |
| ImVoteNet | ✓ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| FCOS3D | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| PointNet++ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Group-Free-3D | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| ImVoxelNet | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| PAConv | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| DGCNN | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |
| SMOKE | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
| PGD | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| MonoFlex | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ |
| SA-SSD | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| FCAF3D | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ |
| PV-RCNN | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Cylinder3D | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ |
| MinkUNet | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ |
| SPVCNN | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ |
| BEVFusion | ✗ | ✗ | ✓ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| CenterFormer | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| TR3D | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ |
| DETR3D | ✓ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| PETR | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
| TPVFormer | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
Note: All the about 500+ models, methods of 90+ papers in 2D detection supported by MMDetection can be trained or used in this codebase.
Please refer to FAQ for frequently asked questions.
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.
The VBTI development team is reviving MMLabs code, making it work with newer PyTorch versions and fixing bugs. We are only a small team, so your help is appreciated.
If you find this project useful in your research, please consider cite:
@misc{mmdet3d2020,
title={{OneDL-MMDetection3D: OneDL Lab} next-generation platform for general {3D} object detection},
author={OneDL-MMDetection3D Contributors},
howpublished = {\url{https://github.com/vbti-development/onedl-mmdetection3d}},
year={2026}
}This project is released under the Apache 2.0 license.
- OneDL-MMEngine: Foundational library for training deep learning models.
- OneDL-MMCV: Foundational library for computer vision.
- OneDL-MMPreTrain: Pre-training toolbox and benchmark.
- OneDL-MMDetection: Detection toolbox and benchmark.
- OneDL-MMDetection3D: 3D Object Detection toolbox and benchmark.
- OneDL-MMRotate: Rotated object detection toolbox and benchmark.
- OneDL-MMSegmentation: Semantic segmentation toolbox and benchmark.
- OneDL-MMDeploy: Model deployment framework.
- OneDL-MIM: MIM installs VBTI packages.