Yunlong Ran, Jing Zeng, Shibo He, Lincheng Li, Yingfeng Chen, Gimhee Lee, Jiming Chen, Qi Ye
This is the official repository for our paper, NeurAR, we release uncertainty verification part and the planned dataset. The planner and the unity module will not release at the current time.
To start, we prefer creating the environment using conda:
conda env create -f environment.ymal
conda activate neurar
Alternatively, you can install them yourself
imageio
imageio-ffmpeg
colorlog
matplotlib
configargparse
tqdm
opencv-python
pandas
jupyter
seaborn
numpy
scikit-image
lpips
- for the NeRF synthetic data
cd data
wget http://cseweb.ucsd.edu/~viscomp/projects/LF/papers/ECCV20/nerf/nerf_example_data.zip
unzip nerf_example_data.zip
- for our collected data, you can download from here.
To verify uncertainty:
cd src
python verify_uncertainty.py --config ../configs/lego.txt
Then launch your jupyter note book and follow the link in your browser
jupyter notebook
Run verify uncertainty.ipynb
jupyter scripts one by one.
For other scene, you can simple replace lego.txt
with {scene}.txt
You can download planned and trained model here and unzip them into /logs
.
And then run:
cd src
python eval.py --config ../configs/cabin.txt
Then run eval.ipynb
jupyter scripts one by one to get metrics.
@ARTICLE{10012495,
author={Ran, Yunlong and Zeng, Jing and He, Shibo and Chen, Jiming and Li, Lincheng and Chen, Yingfeng and Lee, Gimhee and Ye, Qi},
journal={IEEE Robotics and Automation Letters},
title={NeurAR: Neural Uncertainty for Autonomous 3D Reconstruction With Implicit Neural Representations},
year={2023},
volume={8},
number={2},
pages={1125-1132},
doi={10.1109/LRA.2023.3235686}}