3D Cascade RCNN

This is the implementation of 3D Cascade RCNN: High Quality Object Detection in Point Clouds.

We designed a 3D object detection model on point clouds by:

  • Presenting a simple yet effective 3D cascade architecture
  • Analyzing the sparsity of the point clouds and using point completeness score to re-weighting training samples.
    Following is detection results on Waymo Open Dataset.

waymo_scene_1

waymo_scene_2

waymo_scene_3

waymo_scene_4

waymo_scene_5

Results on KITTI

Easy Car Moderate Car Hard Car
AP 11 90.05 86.02 79.27
AP 40 93.20 86.19 83.48

Results on Waymo

Overall Vehicle 0-30m Vehicle 30-50m Vehicle 50m-Inf Vehicle
LEVEL_1 mAP 76.27 92.66 74.99 54.49
LEVEL_2 mAP 67.12 91.95 68.96 41.82

Installation

  1. Requirements.
    The code is tested on the following environment:
  • Ubuntu 16.04 with 4 V100 GPUs
  • Python 3.7
  • Pytorch 1.7
  • CUDA 10.1
  • spconv 1.2.1
  1. Build extensions
python setup.py develop

Getting Started

Prepare for the data.

Please download the official KITTI dataset and generate data infos by following command:

python -m pcdet.datasets.kitti.kitti_dataset create_kitti_infos tools/cfgs/kitti_dataset.yaml

The folder should be like:

data
├── kitti
│   │── ImageSets
│   │── training
│   │   ├──calib & velodyne & label_2 & image_2
│   │── testing
│   │   ├──calib & velodyne & image_2
|   |── kitti_dbinfos_train.pkl
|   |── kitti_infos_train.pkl
|   |── kitti_infos_val.pkl

Training and evaluation.

The configuration file is in tools/cfgs/3d_cascade_rcnn.yaml, and the training scripts is in tools/scripts.

cd tools
sh scripts/3d-cascade-rcnn.sh

Test a pre-trained model

The pre-trained KITTI model is at: model. Run with:

cd tools
sh scripts/3d-cascade-rcnn_test.sh

The evaluation results should be like:

2021-08-10 14:06:14,608   INFO  Car [email protected], 0.70, 0.70:
bbox AP:97.9644, 90.1199, 89.7076
bev  AP:90.6405, 89.0829, 88.4391
3d   AP:90.0468, 86.0168, 79.2661
aos  AP:97.91, 90.00, 89.48
Car [email protected], 0.70, 0.70:
bbox AP:99.1663, 95.8055, 93.3149
bev  AP:96.3107, 92.4128, 89.9473
3d   AP:93.1961, 86.1857, 83.4783
aos  AP:99.13, 95.65, 93.03
Car [email protected], 0.50, 0.50:
bbox AP:97.9644, 90.1199, 89.7076
bev  AP:98.0539, 97.1877, 89.7716
3d   AP:97.9921, 90.1001, 89.7393
aos  AP:97.91, 90.00, 89.48
Car [email protected], 0.50, 0.50:
bbox AP:99.1663, 95.8055, 93.3149
bev  AP:99.1943, 97.8180, 95.5420
3d   AP:99.1717, 95.8046, 95.4500
aos  AP:99.13, 95.65, 93.03

Acknowledge

The code is built on OpenPCDet and Voxel R-CNN.

GitHub

https://github.com/caiqi/Cascasde-3D