rvmde-ii's Introduction

DepthSense

This is the official updated version of DepthSense - the official implementation of "Advancing Autonomous Driving: DepthSense with Radar and Spatial Attention" paper under revision.

🔧 Dependencies and Installation

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

Installation

Clone repo

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

Install dependent packages

pypardiso
tensorboardX
nuscenes-devkit

Dataset

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

Directories

DepthSense/
    data/                           							 
        nuscenes/                 		    
                annotations/
                maps/
                samples/
                sweeps/
                v1.0-trainval/
    dataloader/
    list/
    result/ download and copy the pretrained model here
    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 DepthSense with MER's to generte the dataset for training and evaluation.

Evaluation for DepthSense 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 on interms of day,night,rain change the list path first. The evaluation lists are saved in .\list directory.

jupyter notebook
Evaluate_rvmde.ipynb     #Test

DepthSense with MER's Evalution

Will be updated soon!!!

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}
}

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