A general 3D Object Detection codebase in PyTorch

Det3D is the first 3D Object Detection toolbox which provides off the box implementations of many 3D object detection algorithms such as PointPillars, SECOND, PIXOR, etc, as well as state-of-the-art methods on major benchmarks like KITTI(ViP) and nuScenes(CBGS). Key features of Det3D include the following aspects:

• Multi Datasets Support: KITTI, nuScenes, Lyft
• Point-based and Voxel-based model zoo
• State-of-the-art performance
• DDP & SyncBN

Please refer to INSTALL.md.

Please refer to GETTING_START.md.

We provide many baseline results and trained models. Please refer to MODEL_ZOO.md.

• Models
  • VoxelNet
  • SECOND
  • PointPillars
• Features
  • Multi task learning & Multi-task Learning
  • Distributed Training and Validation
  • SyncBN
  • Flexible anchor dimensions
  • TensorboardX
  • Checkpointer & Breakpoint continue
  • Self-contained visualization
  • Finetune
  • Multiscale Training & Validation
  • Rotated RoI Align

• Models
  • PointRCNN
  • PIXOR

Benjin Zhu , Bingqi Ma

Det3D is released under the MIT license.

• mmdetection
• second.pytorch
• maskrcnn_benchmark