open-mmlab / mmdetection3d

OpenMMLab's next-generation platform for general 3D object detection.

Home Page:https://mmdetection3d.readthedocs.io/en/latest/

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News:

๐Ÿ’Ž Stable version

v1.0.0rc5 was released in 11/10/2022

๐ŸŒŸ Preview of 1.1.x version

A brand new version of MMDetection v1.1.0rc0 was released in 1/9/2022:

  • Unifies interfaces of all components based on MMEngine and MMDet 3.x.
  • A standard data protocol defines and unifies the common keys across different datasets.
  • Faster training and testing speed with more strong baselines.

Find more new features in 1.1.x branch. Issues and PRs are welcome!

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. In this version, we update some of the model checkpoints after the refactor of coordinate systems. See more details in the Changelog.

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.

MMDeploy has supported some MMDetection3d model deployment.

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

Introduction

English | ็ฎ€ไฝ“ไธญๆ–‡

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.

demo image

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 โœ—.

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

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

v1.0.0rc5 was released in 11/10/2022.

Please refer to changelog.md for details and release history.

Benchmark and model zoo

Results and models are available in the model zoo.

Components
Backbones Heads Features
Architectures
3D Object Detection Monocular 3D Object Detection Multi-modal 3D Object Detection 3D Semantic Segmentation
  • Outdoor
  • Indoor
  • Outdoor
  • Indoor
  • Outdoor
  • Indoor
  • Indoor
  • ResNet PointNet++ SECOND DGCNN RegNetX DLA MinkResNet
    SECOND โœ— โœ— โœ“ โœ— โœ— โœ— โœ—
    PointPillars โœ— โœ— โœ“ โœ— โœ“ โœ— โœ—
    FreeAnchor โœ— โœ— โœ— โœ— โœ“ โœ— โœ—
    VoteNet โœ— โœ“ โœ— โœ— โœ— โœ— โœ—
    H3DNet โœ— โœ“ โœ— โœ— โœ— โœ— โœ—
    3DSSD โœ— โœ“ โœ— โœ— โœ— โœ— โœ—
    Part-A2 โœ— โœ— โœ“ โœ— โœ— โœ— โœ—
    MVXNet โœ“ โœ— โœ“ โœ— โœ— โœ— โœ—
    CenterPoint โœ— โœ— โœ“ โœ— โœ— โœ— โœ—
    SSN โœ— โœ— โœ— โœ— โœ“ โœ— โœ—
    ImVoteNet โœ“ โœ“ โœ— โœ— โœ— โœ— โœ—
    FCOS3D โœ“ โœ— โœ— โœ— โœ— โœ— โœ—
    PointNet++ โœ— โœ“ โœ— โœ— โœ— โœ— โœ—
    Group-Free-3D โœ— โœ“ โœ— โœ— โœ— โœ— โœ—
    ImVoxelNet โœ“ โœ— โœ— โœ— โœ— โœ— โœ—
    PAConv โœ— โœ“ โœ— โœ— โœ— โœ— โœ—
    DGCNN โœ— โœ— โœ— โœ“ โœ— โœ— โœ—
    SMOKE โœ— โœ— โœ— โœ— โœ— โœ“ โœ—
    PGD โœ“ โœ— โœ— โœ— โœ— โœ— โœ—
    MonoFlex โœ— โœ— โœ— โœ— โœ— โœ“ โœ—
    SA-SSD โœ— โœ— โœ“ โœ— โœ— โœ— โœ—
    FCAF3D โœ— โœ— โœ— โœ— โœ— โœ— โœ“

    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.

    Model deployment

    Now MMDeploy has supported some MMDetection3D model deployment. Please refer to model_deployment.md for more details.

    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's next-generation platform for general 3D object detection.
    • MMRotate: OpenMMLab rotated object detection 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.
    • MMEditing: OpenMMLab image and video editing toolbox.
    • MMGeneration: OpenMMLab image and video generative models toolbox.
    • MMDeploy: OpenMMLab model deployment framework.

    About

    OpenMMLab's next-generation platform for general 3D object detection.

    https://mmdetection3d.readthedocs.io/en/latest/

    License:Apache License 2.0


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