Mephisto405 / WCMC

Weakly-Supervised Contrastive Learning in Path Manifold for Monte Carlo Image Reconstruction [Cho et al. SIGGRAPH 2021]

WCMC: Weakly-Supervised Contrastive Learning in Path Manifold for Monte Carlo Image Reconstruction

Project Page | Paper

Official PyTorch implementation of WCMC.

Weakly-Supervised Contrastive Learning in Path Manifold for Monte Carlo Image Reconstruction
In-young Cho, Yuchi Huo*, Sung-eui Yoon,*
KAIST, Repulic of Korea
*denotes co-corresponding authors
in SIGGRAPH 2021

Scene credits

• "Library-Home Office" by ThePefDispenser under CC BY 3.0.
• "My Kitchen" by tokabilitor under CC0.

Informal Paper Summary

Our manifold learning framework analyzes light path clusters in Monte Carlo path tracing, resulting in better image reconstruction. Our contrastive method fully utilizes sparse high-dimensional auxiliary features.

Keywords:

• Multi-task learning, Manifold learning
• High-dimensional path features, Path-space manifold, Sparsity
• Deep learning-based noise removal

Quick Start

0. State-of-the-art baselines:

   • Kernel-predicting convolutional network (KPCN) [Bako et al. 2017]
     • Image-space method
   • Sample-based denoising network (SBMC) [Gharbi et al. 2019]
     • Sample-space method
   • Layer-based denoising network (LBMC) [Munkberg and Hasselgren 2020]
     • Sample-space method

1. Clone and build models of your interest; KPCN [Bako et al. 2017], SBMC [Gharbi et al. 2019], and LBMC [Munkberg and Hasselgren 2020]. We modify the original source to be compatible with our code.

2. Run the following demos.

Demo

1. Train the diffuse and specular branches of KPCN-Vanilla simultaneously.

   python train_kpcn.py --single_gpu --batch_size 8 --val_epoch 1 --data_dir /mnt/ssd3/iycho/KPCN \
   --model_name KPCN_vanilla --desc "KPCN vanilla" --num_epoch 8 --lr_dncnn 1e-4 --train_branches
   

2. Fine-tune two branches of KPCN-Vanilla simultaneously.

   python train_kpcn.py --single_gpu --batch_size 8 --val_epoch 1 --data_dir /mnt/ssd3/iycho/KPCN \
   --model_name KPCN_vanilla --desc "KPCN vanilla" --num_epoch 10 --lr_dncnn 1e-6 \
   --start_epoch <??> --best_err <??>
   

3. More demos are presented at train_kpcn.py, train_sbmc.py, and train_lbmc.py.

Tips for Reproducing this Paper

1. Try LBMC first.

   • We found that (unoptimized) LBMC does not require many dependencies and so does our framework (path embedding network + path disentangling loss).

2. Try training your network first and find optimal hyperparameters (e.g., learning rate).

   • Then, attach our manifold framework to your reconstruction model.
   • Use the optimal hyperparameters for the reconstruction network.
   • Please take a look at the paper for other tips, such as setting the manifold-regression balancing parameter of our framework.

3. All you need are the implementations of the ...

   • path descriptor,
   • path embedding network,
   • path disentangling loss, and
   • training framework.
   • The other code is just for visualization, debugging, training monitoring, etc. You may want to ignore it!

Repository Overview

• train_kpcn.py # Train KPCN with/without our manifold learning framework
• train_sbmc.py # Train SBMC with/without our manifold learning framework
• train_lbmc.py # Train LBMC with/without our manifold learning framework
• test_models.py # Test any model
• support/ # All utilities to train/test models
  • datasets.py
  • img_utils.py
  • interfaces.py
  • losses.py
  • metrics.py
  • networks.py
  • utils.py

Citation

@article{cho2021weakly,
  title={Weakly-Supervised Contrastive Learning in Path Manifold for Monte Carlo Image Reconstruction},
  author={Cho, In-Young and Huo, Yuchi and Yoon, Sung-Eui},
  journal={ACM Transactions on Graphics (TOG)},
  volume={40},
  number={4},
  pages={38:1--38:14},
  year={2021},
  publisher={ACM New York, NY, USA},
  url = {https://doi.org/10.1145/3450626.3459876},
}