3D cascade RCNN for object detection on point cloud

Related tags

Deep Learningpoint-cloudobject-detection3d-object-detectioncascade-detection
Overview

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.

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.

Owner
Qi Cai
Qi Cai
GitHub Repository
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