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Radar-Camera Pixel Depth Association for Depth Completion

example figure Example of radar-camera depth completion: (a) raw radar depth, (b) enhanced radar depth, (c) final predicted depth.

Directories

rc-pda/
    data/                           							 
        nuscenes/                 		    
                annotations/
                maps/
                samples/
                sweeps/
                v1.0-trainval/
    lib/
    scripts/
    external/                   				   	        
        panoptic-deeplab/       
        RAFT/

Setup

Create a conda environment called pda

conda create -n pda python=3.6

Install required packages

pip install -r requirements.txt

Download nuScenes dataset (Full dataset (v1.0)) into data/nuscenes/
Clone external repos Panoptic-DeepLab and RAFT into external/

Code

1. Data preparation

cd scripts

# 1) split data
python split_trainval.py

# 2) extract images for flow computation
python prepare_flow_im.py

# 3) compute image flow from im1 to im2
python cal_flow.py 

# 4) compute camera intrinsic matrix and transformation from cam1 to cam2
python cal_cam_matrix.py 

# 5) transform image flow to normalized expression (u2,v2)
python cal_im_flow2uv.py  

# 6) compute vehicle semantic segmentation
python semantic_seg.py 

# 7) compute dense ground truth (depth1, u2, v2) and low height mask
python cal_gt.py  

# 8) compute merged radar (5 frames)
python cal_radar.py       

# 9) create .h5 dataset file
python gen_h5_file3.py

2. Estimate radar camera association

python train_pda.py        # train
python test_pda.py         # test

Download pre-trained weights

3. Generate enhanced radar depth (RC-PDA)

python cal_mer.py

4. Train depth completion by using the enhanced depth

Depth completion scheme 1 (Using depths and RGB as input channels)

python train_depth.py        	# train
python test_depth.py         	# test

Download pre-trained weights

Depth completion scheme 2 (Multi-Scale Guided Cascade Hourglass Network)

python train_depth_hg.py        # train
python test_depth_hg.py         # test

Download pre-trained weights.

Citation

@InProceedings{Long_2021_CVPR,
    author    = {Long, Yunfei and Morris, Daniel and Liu, Xiaoming and Castro, Marcos and Chakravarty, Punarjay and Narayanan, Praveen},
    title     = {Radar-Camera Pixel Depth Association for Depth Completion},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {12507-12516}
}