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Official implementation of "S-VolSDF: Sparse Multi-View Stereo Regularization of Neural Implicit Surfaces" (ICCV 2023)

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S-VolSDF

This is the official implementation of S-VolSDF: Sparse Multi-View Stereo Regularization of Neural Implicit Surfaces.

Haoyu Wu, Alexandros Graikos, Dimitris Samaras

International Conference on Computer Vision (ICCV), 2023


Installation

The code is compatible with python 3.6, CUDA 11.1, and pytorch 1.9.0.

conda create --name s_volsdf python=3.6
conda activate s_volsdf
pip install -r requirements.txt

Data

Download data_s_volsdf, inlcuding the DTU and the BlendedMVS Dataset. Then,

unzip data_s_volsdf.zip -d ./

Train

# DTU
python runner.py testlist=scan106 outdir=exps_mvs exps_folder=exps_vsdf opt_stepNs=[100000,0,0]
# BlendedMVS (BMVS)
python runner.py vol=bmvs testlist=scan4 outdir=exps_mvs exps_folder=exps_vsdf opt_stepNs=[100000,0,0]
  • Train all scans sequentially: testlist='config/lists/dtu.txt' or testlist='config/lists/bmvs.txt'
  • Check more details in ./config

Eval - 3D Reconstruction

# DTU  
python evals/eval_dtu.py --datadir exps_mvs --scan 106 --data_dir_root data_s_volsdf
# BlendedMVS (BMVS) 
python evals/eval_bmvs.py --datadir exps_mvs --scan 4 --data_dir_root data_s_volsdf
  • Evaluate all scans: --scan -1
  • The metric is the Chamfer distance (in mm) of the output point clouds .

Eval - Novel View Synthesis

  1. Render and save RGBD
# DTU
python eval_vsdf.py --conf dtu --eval_rendering --data_dir_root data_s_volsdf --expname ours --exps_folder exps_vsdf --evals_folder exps_result 
# BMVS: --conf bmvs
  1. Simple image-based rendering
# prepare data
python runner.py +create_scene=true outdir=exps_ibr testlist='config/lists/dtu.txt' # DTU
python runner.py vol=bmvs +create_scene=true outdir=exps_ibr testlist='config/lists/bmvs.txt' # BMVS

# image-based rendering
python simple_ibr.py outdir=exps_ibr +evals_folder=exps_result testlist='config/lists/dtu.txt' # DTU
python simple_ibr.py vol=bmvs outdir=exps_ibr +evals_folder=exps_result testlist='config/lists/bmvs.txt' # BMVS
  1. Evaluate
# DTU
python eval_vsdf.py --conf dtu --data_dir_root data_s_volsdf --eval_rendering --expname ours --exps_folder exps_vsdf --evals_folder exps_result --result_from blend
# BMVS: --conf bmvs

Acknowledgements

  • This work was supported in part by the NASA Biodiversity Program (Award 80NSSC21K1027), and NSF Grant IIS-2212046.
  • We also thank Alfredo Rivero for his thoughtful feedback and meticulous proofreading.
  • We borrowed code from VolSDF, CasMVSNet, UCSNet, TransMVSNet, and DTUeval-python. We thank all the authors for their great work and repos.

Citation

If you find our code useful for your research, please cite

@article{wu2023s,
  title={S-VolSDF: Sparse Multi-View Stereo Regularization of Neural Implicit Surfaces},
  author={Wu, Haoyu and Graikos, Alexandros and Samaras, Dimitris},
  journal={arXiv preprint arXiv:2303.17712},
  year={2023}
}

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Official implementation of "S-VolSDF: Sparse Multi-View Stereo Regularization of Neural Implicit Surfaces" (ICCV 2023)

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