Det3D
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
Model Zoo and Baselines
Second on KITTI(val) Dataset
car AP @0.70, 0.70, 0.70:
bbox AP:90.54, 89.35, 88.43
bev AP:89.89, 87.75, 86.81
3d AP:87.96, 78.28, 76.99
aos AP:90.34, 88.81, 87.66
PointPillars on KITTI(val) Dataset
car [email protected], 0.70, 0.70:
bbox AP:90.63, 88.86, 87.35
bev AP:89.75, 86.15, 83.00
3d AP:85.75, 75.68, 68.93
aos AP:90.48, 88.36, 86.58
To Be Released
- PointPillars on NuScenes(val) Dataset
- CGBS on NuScenes(val) Dataset
- CGBS on Lyft(val) Dataset
Currently Support
- Models
- [x] VoxelNet
- [x] SECOND
- [x] PointPillars
- Features
- [x] Multi task learning & Multi-task Learning
- [x] Distributed Training and Validation
- [x] SyncBN
- [x] Flexible anchor dimensions
- [x] TensorboardX
- [x] Checkpointer & Breakpoint continue
- [x] Self-contained visualization
- [x] Finetune
- [x] Multiscale Training & Validation
- [x] Rotated RoI Align
TODO List
- Models
- [ ] PointRCNN
- [ ] PIXOR