# open-mmlab/mmdetection3d

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

Repository: https://github.com/open-mmlab/mmdetection3d
Canonical: https://ross.abutalabs.com/products/mmdetection3d
Homepage: https://mmdetection3d.readthedocs.io/en/latest/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: pytorch, 3d-object-detection, object-detection, point-cloud
Last push: 2024-07-10T15:19:34+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2247, "days_push": 784, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 6518, forks 1786 (observed 2026-08-28T04:09:44.859812+00:00)

## What it is
MMDetection3D is OpenMMLab's next-generation platform for general 3D object detection, built on PyTorch. It provides a modular toolbox with a model zoo covering LiDAR-based, camera-based, and multi-modal 3D detection methods on standard benchmarks like nuScenes, KITTI, and Waymo.

## Use cases
- train 3d object detection models on point clouds
- run lidar-based detection on nuScenes or KITTI
- evaluate 3d detection models on a benchmark
- detect objects from multi-view camera images
- fine-tune a pretrained 3d detection model
- benchmark point cloud detection methods
- build a custom 3d detection pipeline for autonomous driving

## When to choose
- you need a modular, config-driven framework for 3D detection research
- you want pretrained models and a large model zoo for LiDAR or camera-based detection
- you work with standard autonomous driving datasets like nuScenes, KITTI, or Waymo
- you want consistency with the OpenMMLab ecosystem (mmcv, mmdetection)

## When to avoid
- you need simple inference in production with minimal dependencies
- you work outside 3D detection, e.g. 2D detection or segmentation only
- you prefer lightweight PyTorch codebases you can modify freely
- you need Windows support or non-CUDA hardware

## Facets
- artifact type: framework
- maturity: active
- function: machine-learning, deep-learning, computer-vision
- domain: computer-vision, autonomous-vehicles, deep-learning, machine-learning
- platform: python
- tags: 3d-object-detection, point-cloud, lidar, pytorch, model-zoo, mmlab, gpu, linux

## Member repositories
- open-mmlab/mmdetection3d (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:44.859812+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:44:25.797183+00:00, confidence not recorded.
  - readme: https://github.com/open-mmlab/mmdetection3d (fetched 2026-08-28T04:09:44.859812+00:00, sha 40edd8ca2ec2)
- Data as of 2026-08-30T08:39:29.467469+00:00.