open-mmlab/mmdetection3d is a AI Tools project on GitHub, written primarily in Python. It has 6.6k stars. OpenMMLab's next-generation platform for general 3D object detection.
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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.
The main branch works with PyTorch 1.8+.
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.
What's New
Highlight
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.
v1.4.0 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
Installation
Please refer to Installation for installation instructions.
Getting Started
For detailed user guides and advanced guides, please refer to our documentation:
User Guides
Train & Test
Learn about Configs
Coordinate System
Dataset Preparation
Customize Data Pipelines
Test and Train on Standard Datasets
Inference
Train with Customized Datasets
Useful Tools
Advanced Guides
Datasets
KITTI Dataset
NuScenes Dataset
Lyft Dataset
Waymo Dataset
SUN RGB-D Dataset
ScanNet Dataset
S3DIS Dataset
SemanticKITTI Dataset
Supported Tasks
LiDAR-Based 3D Detection
Vision-Based 3D Detection
LiDAR-Based 3D Semantic Segmentation
Customization
Customize Datasets
Customize Models
Customize Runtime Settings
Overview of Benchmark and Model Zoo
Results and models are available in the model zoo.
Components
Backbones
Heads
Features
PointNet (CVPR'2017)
PointNet++ (NeurIPS'2017)
RegNet (CVPR'2020)
DGCNN (TOG'2019)
DLA (CVPR'2018)
MinkResNet (CVPR'2019)
MinkUNet (CVPR'2019)
Cylinder3D (CVPR'2021)
FreeAnchor (NeurIPS'2019)
Dynamic Voxelization (CoRL'2019)
Architectures
LiDAR-based 3D Object Detection
Camera-based 3D Object Detection
Multi-modal 3D Object Detection
3D Semantic Segmentation
Outdoor
SECOND (Sensor'2018)
PointPillars (CVPR'2019)
SSN (ECCV'2020)
3DSSD (CVPR'2020)
SA-SSD (CVPR'2020)
PointRCNN (CVPR'2019)
Part-A2 (TPAMI'2020)
CenterPoint (CVPR'2021)
PV-RCNN (CVPR'2020)
CenterFormer (ECCV'2022)
Indoor
VoteNet (ICCV'2019)
H3DNet (ECCV'2020)
Group-Free-3D (ICCV'2021)
FCAF3D (ECCV'2022)
TR3D (ArXiv'2023)
Outdoor
ImVoxelNet (WACV'2022)
SMOKE (CVPRW'2020)
FCOS3D (ICCVW'2021)
PGD (CoRL'2021)
MonoFlex (CVPR'2021)
DETR3D (CoRL'2021)
PETR (ECCV'2022)
Indoor
ImVoxelNet (WACV'2022)
Outdoor
MVXNet (ICRA'2019)
BEVFusion (ICRA'2023)
Indoor
ImVoteNet (CVPR'2020)
Outdoor
MinkUNet (CVPR'2019)
SPVCNN (ECCV'2020)
Cylinder3D (CVPR'2021)
TPVFormer (CVPR'2023)
Indoor
PointNet++ (NeurIPS'2017)
PAConv (CVPR'2021)
DGCNN (TOG'2019)
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.
FAQ
Please refer to FAQ for frequently asked questions.
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.
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}
}
License
This project is released under the Apache 2.0 license.
Projects in OpenMMLab
MMEngine: OpenMMLab foundational library for training deep learning models.
MMCV: OpenMMLab foundational library for computer vision.
MMEval: A unified evaluation library for multiple machine learning libraries.
MIM: MIM installs OpenMMLab packages.
MMPreTrain: OpenMMLab pre-training toolbox and benchmark.
MMDetection: OpenMMLab detection toolbox and benchmark.
MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
MMRotate: OpenMMLab rotated object detection toolbox and benchmark.
MMYOLO: OpenMMLab YOLO series toolbox and benchmark.
MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
MMOCR: OpenMMLab text detection, recognition, and understanding toolbox.
MMPose: OpenMMLab pose estimation toolbox and benchmark.
MMHuman3D: OpenMMLab 3D human parametric model toolbox and benchmark.
MMSelfSup: OpenMMLab self-supervised learning toolbox and benchmark.
MMRazor: OpenMMLab model compression toolbox and benchmark.
MMFewShot: OpenMMLab fewshot learning toolbox and benchmark.
MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
MMTracking: OpenMMLab video perception toolbox and benchmark.
MMFlow: OpenMMLab optical flow toolbox and benchmark.
MMagic: OpenMMLab Advanced, Generative and Intelligent Creation toolbox.
MMGeneration: OpenMMLab image and video generative models toolbox.
How active is development on open-mmlab/mmdetection3d?
The most recent commit recorded on open-mmlab/mmdetection3d was 2.3 years ago, based on the GitHub push timestamp. The repository has 1.8k forks — one of the better signals of community interest.
How many stars does open-mmlab/mmdetection3d have?
open-mmlab/mmdetection3d has 6.6k GitHub stars — refresh the page for the live number, or check github.com/open-mmlab/mmdetection3d. TopGit mirrors GitHub's count but does not claim minute-by-minute accuracy.
Is open-mmlab/mmdetection3d open source?
Yes — open-mmlab/mmdetection3d ships under the Apache-2.0 license, which makes its source code freely readable (and, depending on license terms, forkable and reusable). Source: github.com/open-mmlab/mmdetection3d.
What else is in the AI Tools space?
open-mmlab/mmdetection3d is tracked by TopGit under the AI Tools category, alongside 4 GitHub-tagged topics. Trending and Topics pages list peer repositories of comparable stars and language.
What topics is open-mmlab/mmdetection3d associated with?
GitHub's repository topics for open-mmlab/mmdetection3d: "3d-object-detection", "object-detection", "point-cloud", "pytorch". TopGit's editorial category is AI Tools.
Where can I see open-mmlab/mmdetection3d in action?
The project maintains a homepage at https://mmdetection3d.readthedocs.io/en/latest/. The README tab on this page also usually contains screenshots and a quickstart.
Where do I read more about open-mmlab/mmdetection3d?
This TopGit page is a snapshot — the READ ME tab shows the project's own README content (links stripped, images preserved). The GitHub repository at github.com/open-mmlab/mmdetection3d is the definitive source.
Read full README in the tab above.
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