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新闻: 我们发布了版本 v1.0.0rc6.

💎 稳定版本

最新的 1.0.0rc6 版本已经在 2022.12.2 发布。

🌟 1.1.x 预览版本

全新的 v1.1.0rc0 版本已经在 2022.9.1 发布:

  • 基于 MMEngineMMDet 3.x 统一了各组件接口。
  • 通过一个标准的数据格式定义和统一了不同数据集的通用内容。
  • 实现了更快的训练和测试速度,并提供了更多强大的基准模型。

请在 1.1.x 分支 中发现更多新功能。 欢迎提出 issue 和 PR!

由于坐标系的统一和简化,模型的兼容性会受到影响。目前,大多数模型都以类似的性能对齐了精度,但仍有少数模型在进行基准测试。在这个版本中,我们更新了部分坐标系重构后的模型权重文件。您可以在 变更日志 中查看更多详细信息。

在第三届 nuScenes 3D 检测挑战赛(第五届 AI Driving Olympics, NeurIPS 2020)中,我们获得了最佳 PKL 奖、第三名和最好的纯视觉的结果,相关的代码和模型将会在不久后发布。

最好的纯视觉方法 FCOS3D 的代码和模型已经发布。请继续关注我们的多模态检测器 MoCa

MMDeploy 已经支持了部分 MMDetection3D 模型的部署。

文档: https://mmdetection3d.readthedocs.io/

简介

English | 简体中文

主分支代码目前支持 PyTorch 1.3 以上的版本。

MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱, 下一代面向3D检测的平台. 它是 OpenMMlab 项目的一部分,这个项目由香港中文大学多媒体实验室和商汤科技联合发起.

demo image

主要特性

  • 支持多模态/单模态的检测器

    支持多模态/单模态检测器,包括 MVXNet,VoteNet,PointPillars 等。

  • 支持户内/户外的数据集

    支持室内/室外的3D检测数据集,包括 ScanNet, SUNRGB-D, Waymo, nuScenes, Lyft, KITTI.

    对于 nuScenes 数据集, 我们也支持 nuImages 数据集.

  • 与 2D 检测器的自然整合

    MMDetection 支持的300+个模型 , 40+的论文算法, 和相关模块都可以在此代码库中训练或使用。

  • 性能高

    训练速度比其他代码库更快。下表可见主要的对比结果。更多的细节可见基准测评文档。我们对比了每秒训练的样本数(值越高越好)。其他代码库不支持的模型被标记为

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

MMDetectionMMCV 一样, MMDetection3D 也可以作为一个库去支持各式各样的项目.

开源许可证

该项目采用 Apache 2.0 开源许可证

更新日志

最新的版本 v1.0.0rc5 在 2022.10.11 发布。

如果想了解更多版本更新细节和历史信息,请阅读更新日志

基准测试和模型库

测试结果和模型可以在模型库中找到。

模块组件
主干网络 检测头 特性
算法模型
3D 目标检测 单目 3D 目标检测 多模态 3D 目标检测 3D 语义分割
室外 室内 室外 室内 室外 室内 室内
ResNet PointNet++ SECOND DGCNN RegNetX DLA MinkResNet
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

注意: MMDetection 支持的基于2D检测的300+个模型 , 40+的论文算法在 MMDetection3D 中都可以被训练或使用。

安装

请参考快速入门文档进行安装。

快速入门

请参考快速入门文档学习 MMDetection3D 的基本使用。 我们为新手提供了分别针对已有数据集新数据集的使用指南。我们也提供了一些进阶教程,内容覆盖了学习配置文件, 增加数据集支持, 设计新的数据预处理流程, 增加自定义模型, 增加自定义的运行时配置Waymo 数据集.

请参考 FAQ 查看一些常见的问题与解答。在升级 MMDetection3D 的版本时,请查看兼容性文档以知晓每个版本引入的不与之前版本兼容的更新。

模型部署

现在 MMDeploy 已经支持了一些 MMDetection3D 模型的部署。请参考 model_deployment.md了解更多细节。

引用

如果你觉得本项目对你的研究工作有所帮助,请参考如下 bibtex 引用 MMdetection3D

@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}
}

贡献指南

我们感谢所有的贡献者为改进和提升 MMDetection3D 所作出的努力。请参考贡献指南来了解参与项目贡献的相关指引。

致谢

MMDetection3D 是一款由来自不同高校和企业的研发人员共同参与贡献的开源项目。我们感谢所有为项目提供算法复现和新功能支持的贡献者,以及提供宝贵反馈的用户。我们希望这个工具箱和基准测试可以为社区提供灵活的代码工具,供用户复现已有算法并开发自己的新的 3D 检测模型。

OpenMMLab 的其他项目

  • MMCV: OpenMMLab 计算机视觉基础库
  • MIM: MIM 是 OpenMMlab 项目、算法、模型的统一入口
  • MMClassification: OpenMMLab 图像分类工具箱
  • MMDetection: OpenMMLab 目标检测工具箱
  • MMDetection3D: OpenMMLab 新一代通用 3D 目标检测平台
  • MMRotate: OpenMMLab 旋转框检测工具箱与测试基准
  • MMSegmentation: OpenMMLab 语义分割工具箱
  • MMOCR: OpenMMLab 全流程文字检测识别理解工具包
  • MMPose: OpenMMLab 姿态估计工具箱
  • MMHuman3D: OpenMMLab 人体参数化模型工具箱与测试基准
  • MMSelfSup: OpenMMLab 自监督学习工具箱与测试基准
  • MMRazor: OpenMMLab 模型压缩工具箱与测试基准
  • MMFewShot: OpenMMLab 少样本学习工具箱与测试基准
  • MMAction2: OpenMMLab 新一代视频理解工具箱
  • MMTracking: OpenMMLab 一体化视频目标感知平台
  • MMFlow: OpenMMLab 光流估计工具箱与测试基准
  • MMEditing: OpenMMLab 图像视频编辑工具箱
  • MMGeneration: OpenMMLab 图片视频生成模型工具箱
  • MMDeploy: OpenMMLab 模型部署框架

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扫描下方的二维码可关注 OpenMMLab 团队的 知乎官方账号,加入 OpenMMLab 团队的 官方交流 QQ 群

我们会在 OpenMMLab 社区为大家

  • 📢 分享 AI 框架的前沿核心技术
  • 💻 解读 PyTorch 常用模块源码
  • 📰 发布 OpenMMLab 的相关新闻
  • 🚀 介绍 OpenMMLab 开发的前沿算法
  • 🏃 获取更高效的问题答疑和意见反馈
  • 🔥 提供与各行各业开发者充分交流的平台

干货满满 📘,等你来撩 💗,OpenMMLab 社区期待您的加入 👬

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简介

基于 PyTorch 和 MMCV 的通用 3D 感知算法库,支持室内外场景多个数据集的 3D 目标检测和 3D 点云分割,同时支持各种单模态和多模态算法,和 MMDetection 中各种 2D 检测算法模块的无缝衔接,为各种 3D 感知任务的算法研发提供了一套统一化、标准化和可复现的高性能基准。 展开 收起
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