RVMDE

Official implementation of "RVMDE : Radar Validated Monocular Depth Estimation for Robotics", https://arxiv.org/abs/2109.05265v1.

🔧 Dependencies and Installation

• Python >= 3.7 (Recommend to use Anaconda or Miniconda)
• PyTorch >= 1.9

Installation

1. Clone repo

   git clone https://github.com/MI-Hussain/RVMDE

2. Install dependent packages

pypardiso
tensorboardX
nuscenes-devkit

Dataset

Download nuScenes dataset (Full dataset (v1.0)) into data/nuscenes/

Directories

rvmde/
    data/                           							 
        nuscenes/                 		    
                annotations/
                maps/
                samples/
                sweeps/
                v1.0-trainval/
    dataloader/
    list/
    result/
    model/                   				   	        
                   	     				

Dataset Prepration use the externel repos

Please follow external repos (https://github.com/lochenchou/DORN_radar) for Height Extension and (https://github.com/longyunf/rc-pda) for RVMDE with MER's to generte the dataset for training and evaluation.

Evaluation for RVMDE on nuScenes

Download pre-trained weights

Modifying dataset path in valid_loader.py, evalutation list path in data_loader.py, pretrained_weights path in Evalutation_rvmde.py file to evalute.

For evaluation of day,night,rain change the list path first. The evaluation lists are saved in .\list directory.

Evaluation_rvmde.ipynb                  #Evaluation

Evaluation for RVMDE with MERs on nuScenes

Please visit this work (https://github.com/longyunf/rc-pda) for detail information of data prepration of training and evaluation sets.

Download pre-trained RVMDE with MERs weights

Evaluation_RVMDE_with_MERS.ipynb      #Evaluation

Citation

@Article{hussain2021rvmde,
    title={RVMDE : Radar Validated Monocular Depth Estimation for Robotics},
    author={Muhammad Ishfaq Hussain, Muhammad Aasim Rafique and Moongu Jeon},
    journal={arXiv:2109.05265v1},
    year={2021}
}

References

The following works have been used by RVMDE:

@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}
}

@INPROCEEDINGS{9506550,
author={Lo, Chen-Chou and Vandewalle, Patrick},
booktitle={2021 IEEE International Conference on Image Processing (ICIP)}, 
title={Depth Estimation From Monocular Images And Sparse Radar Using Deep Ordinal Regression Network}, 
year={2021},
pages={3343-3347},
doi={10.1109/ICIP42928.2021.9506550}
}