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LiCROcc

arXiv web star

This repository contains the implementation of the paper.

If you find our work useful, Please give us a star 🌟!

LiCROcc: Teach Radar for Accurate Semantic Occupancy Prediction using LiDAR and Camera
Yukai Ma1,2, Jianbiao Mei1,2, Xuemeng Yang2, Licheng Wen2, Weihua Xu1, Jiangning Zhang1, Botian Shi2,^, Yong Liu1,^, Xingxing Zuo3
1ZJU 2PJLab3TUM
^Corresponding Authors

Plan

We will release the code for radar SSC in this repo.

Getting Started

Installation

Please refer to SSC-RS

  • spconv-cu111==2.1.25
  • torch-scatter==2.0.8
  • torchmetrics>=0.9.0

Prepare Dataset

-Please refer to OpenOccupancy to prepare nuScenes dataset.

  • Please refer to CRN to generate radar point cloud in BEV view.
python scripts/gen_radar_bev.py  # accumulate sweeps and transform to LiDAR coords

Folder structure:

LiCROcc
├── data/
│   ├── nuscenes/
│   │   ├── maps/
│   │   ├── samples/
│   │   ├── sweeps/
│   │   ├── lidarseg/
│   │   ├── v1.0-test/
│   │   ├── v1.0-trainval/
│   │   ├── nuscenes_occ_infos_train.pkl/
│   │   ├── nuscenes_occ_infos_val.pkl/
│   │   ├── radar_bev_filter/

Run and Eval

Download Teacher model here

Train RC-LiCROcc

./tools/dist_train.sh ./projects/configs/ssc_rs/ssc_rs_base_nuscenes_LC2LR123.py N_GPUs

Train R-LiCROcc

./tools/dist_train.sh ./projects/configs/ssc_rs/ssc_rs_base_nuscenes_LC2radar12.py N_GPUs

Eval RC-LiCROcc

./tools/dist_test.sh ./projects/configs/ssc_rs/ssc_rs_base_nuscenes_LC2LR123.py ./path/to/ckpts.pth N_GPUs

Eval R-LiCROcc

./tools/dist_test.sh ./projects/configs/ssc_rs/ssc_rs_base_nuscenes_LC2radar12.py ./path/to/ckpts.pth N_GPUs

Model Zoo

coming soon

Cite Us

@misc{ma2024licroccteachradaraccurate,
title={LiCROcc: Teach Radar for Accurate Semantic Occupancy Prediction using LiDAR and Camera}, 
author={Yukai Ma and Jianbiao Mei and Xuemeng Yang and Licheng Wen and Weihua Xu and Jiangning Zhang and Botian Shi and Yong Liu and Xingxing Zuo},
year={2024},
eprint={2407.16197},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2407.16197}, 
}  

Credit

We adopt the following open-sourced projects:

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