General 3d object detection code for kitti dataset and nuscene dataset. Code to implement 3d object detection code for better understanding and cleaning.
conda install scikit-image scipy numba pillow matplotlibpip install fire tensorboardX protobuf opencv-python- KITTI Dataset Preparation
Download KITTI dataset and create some directories first:
|── KITTI_DATASET_ROOT
├── Train <-- 7481 train data
| ├── image_2 <-- for visualization
| ├── calib
| ├── label_2
| ├── velodyne
| └── velodyne_reduced <-- empty directory
└── Test <-- 7580 test data
├── image_2 <-- for visualization
├── calib
├── velodyne
└── velodyne_reduced <-- empty directory
- Nuscenes Dataset Preparation
Download Nuscenes dataset and create some directories first:
# For nuScenes Dataset │
└── NUSCENES_TRAINVAL_DATASET_ROOT
├── samples <-- key frames
├── sweeps <-- frames without annotation
├── maps <-- unused
└── v1.0-trainval <-- metadata and annotations
└── NUSCENES_TEST_DATASET_ROOT
├── samples <-- key frames
├── sweeps <-- frames without annotation
├── maps <-- unused
└── v1.0-test <-- metadata
- convert to pickle file
# KITTI
python create_data.py kitti_data_prep --root_path=KITTI_DATASET_ROOT
# nuScenes
python create_data.py nuscenes_data_prep --root_path=NUSCENES_TRAINVAL_DATASET_ROOT --version="v1.0-trainval" --nsweeps=10
python create_data.py nuscenes_data_prep --root_path=NUSCENES_TEST_DATASET_ROOT --version="v1.0-test" --nsweeps=10
- VoxelNet
| Name | db_sample | subset | mAP | NDS |
|---|---|---|---|---|
| VoxelNet | True | True | 45.33 | 58.12 |
| SequenceVoxelNet | True | True |