hg-chung / dpir

Differentiable Point-based Inverse Rendering

Differentiable Point-based Inverse Rendering

Project | Paper

This repository contains the implementation of the paper:

Differentiable Point-based Inverse Rendering

Hoon-Gyu Chung, Seokjun Choi, Seung-Hwan Baek

CVPR, 2024

Abstract

We present differentiable point-based inverse rendering, DPIR, an analysis-by-synthesis method that processes images captured under diverse illuminations to estimate shape and spatially-varying BRDF. To this end, we adopt point-based rendering, eliminating the need for multiple samplings per ray, typical of volumetric rendering, thus significantly enhancing the speed of inverse rendering. To realize this idea, we devise a hybrid point-volumetric representation for geometry and a regularized basis-BRDF representation for reflectance. The hybrid geometric representation enables fast rendering through point-based splatting while retaining the geometric details and stability inherent to SDF-based representations. The regularized basis-BRDF mitigates the ill-posedness of inverse rendering stemming from limited light-view angular samples. We also propose an efficient shadow detection method using point-based shadow map rendering. Our extensive evaluations demonstrate that DPIR outperforms prior works in terms of reconstruction accuracy, computational efficiency, and memory footprint. Furthermore, our explicit point-based representation and rendering enables intuitive geometry and reflectance editing.

Installation

We recommend you to use Conda environment. Install pytorch3d following INSTALL.md.

conda create -n DPIR python=3.9
conda activate DPIR
conda install pytorch=1.13.0 torchvision pytorch-cuda=11.6 -c pytorch -c nvidia
conda install -c fvcore -c iopath -c conda-forge fvcore iopath
conda install numpy matplotlib tqdm imageio
pip install scikit-image plotly opencv-python pyhocon open3d lpips kornia icecream
conda install pytorch3d -c pytorch3d

Dataset

We utilized multi-view multi-light image dataset(DiLiGenT-MV Dataset) and photometric image dataset.

Multi-view multi-light image dataset was preprocessed following PS-NeRF, which contains 5 objects.

Photometric image dataset was rendered by Blender, which contains 4 objects.

You can download dataset from Google Drive and put them in the corresponding folder.

Train and Evaluation

You can train multi-view multi-light dataset(DiLiGenT-MV Dataset) or photometric dataset.

If you want to train and evaluate DiLiGenT-MV dataset,

cd code_diligent
python main.py --conf confs/buddha.conf --datadir DiLiGenT-MV --dataname buddha --basedir output
python evaluation.py --conf confs/buddha.conf --datadir DiLiGenT-MV --dataname buddha --basedir output

If you want to train and evaluate photometric dataset,

cd code_photometric
python main.py --conf confs/maneki.conf --datadir Photometric --dataname maneki --basedir output
python evaluation.py --conf confs/maneki.conf --datadir Photometric --dataname maneki --basedir output

Result

Citation

If you find this work useful in your research, please consider citing:

@inproceedings{chung2023differentiable,
  title={Differentiable Point-based Inverse Rendering},
  author={Chung, Hoon-Gyu and Choi, Seokjun and Baek, Seung-Hwan},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2024}
}

Acknowledgement

Part of our code is based on the previous works: point-radiance, PS-NeRF, and PhySG.

dpir's Issues

Typo in code_photometric/main.py

Line 27 Trainder -> Trainer