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ShadowNeuS

Code for our CVPR 2023 paper "ShadowNeuS: Neural SDF Reconstruction by Shadow Ray Supervision", which draws inspiration from NeRF and presents a new ray supervision scheme for reconstructing scenes from single-view shadows.

Project Page | Paper | Video | Dataset

https://user-images.githubusercontent.com/25758401/231767821-c03510b1-a087-4f7c-bdac-959b2466280d.mp4

Usage

Setup

git clone https://github.com/gerwang/ShadowNeuS.git
cd ShadowNeuS
conda create -n shadowneus python=3.9
conda activate shadowneus
pip install -r requirements.txt

Testing

Here we show how to test our code on an example scene. Before testing, you need to

• Download the example data and unzip it to ./public_data/nerf_synthetic.

• Download the pretrained checkpoint of lego_specular_point here and unzip it to ./exp.

Novel-view synthesis

python exp_runner.py --mode validate_view --conf confs/point_color.conf --case lego_specular_point --is_continue --data_sub 1 --test_mode

See the results at ./exp/lego_specular_point/point_color/novel_view/validations_fine/.

Extracting mesh

python exp_runner.py --mode validate_mesh --conf confs/point_color.conf --case lego_specular_point --is_continue --data_sub 1 --test_mode

See the results at ./exp/lego_specular_point/point_color/meshes/00150000.ply.

Relighting

python exp_runner.py --mode validate_relight --conf confs/point_color.conf --case lego_specular_point --is_continue --data_sub 1 --test_mode

See the results at ./exp/lego_specular_point/point_color/novel_light/validations_fine/.

Material editing

python exp_runner.py --mode validate_relight_0_point_gold --conf confs/point_color.conf --case lego_specular_point --is_continue --test_mode

See the results at ./exp/lego_specular_point/point_color/novel_light_gold/validations_fine/.

The --mode option can be validate_relight_<img_idx>_<light>_<material>, where <img_idx> is the image index in the training dataset, light can be point or dir which determines whether a point light or direction light is used, and material can be gold or emerald.

Evaluate normal and depth map

python exp_runner.py --mode validate_normal_depth --conf confs/point_color.conf --case lego_specular_point --is_continue --data_sub 1 --test_mode

See the results at ./exp/lego_specular_point/point_color/quantitative_compare/.

Environment relighting

python exp_runner.py --mode validate_env_0_0 --conf confs/point_color.conf --case lego_specular_point --is_continue --data_sub 1 --test_mode
python exp_runner.py --mode validate_env_0_0.25 --conf confs/point_color.conf --case lego_specular_point --is_continue --data_sub 1 --test_mode
python exp_runner.py --mode validate_env_0 --conf confs/point_color.conf --case lego_specular_point --is_continue --data_sub 1 --test_mode
python exp_runner.py --mode validate_env_0_0.75 --conf confs/point_color.conf --case lego_specular_point --is_continue --data_sub 1 --test_mode

Download environment maps of Industrial Workshop Foundry, Thatch Chapel, Blaubeuren Night and J&E Gray Park. Extract them to ./public_data/envmap.

python env_relight.py --work_path ./exp/lego_specular_point/point_color/ --env_paths ./public_data/envmap/industrial_workshop_foundry_4k.exr,./public_data/envmap/thatch_chapel_4k.exr,./public_data/envmap/blaubeuren_night_4k.exr,./public_data/envmap/je_gray_park_4k.exr --save_names super_workshop,super_chapel,super_night,super_park --super_sample --n_theta 128 --n_frames 128 --device_ids 0,1,2,3

See the results at ./exp/lego_specular_point/point_color/super_workshop, super_chapel, super_night, and super_park. The above command is tested on four RTX 3090 GPUs.

Output:

https://user-images.githubusercontent.com/25758401/231927850-cd4ace70-b034-4aed-b1b4-8140c1338bec.mp4

Training

You can download training data from here.

On point light RGB inputs

Extract point_light.zip and move each scene to ./public_data/nerf_synthetic. Then run

python exp_runner.py --mode train --conf ./confs/point_color.conf --case <case_name>_specular_point

On point light shadow inputs

Extract point_light.zip and move each scene to ./public_data/nerf_synthetic. Then run

python exp_runner.py --mode train --conf ./confs/point_shadow.conf --case <case_name>_specular_point

On directional light RGB inputs

Extract directional_light.zip and move each scene to ./public_data/nerf_synthetic. Then run

python exp_runner.py --mode train --conf ./confs/directional_color.conf --case <case_name>_specular

On directional light shadow inputs

Extract directional_light.zip and move each scene to ./public_data/nerf_synthetic. Then run

python exp_runner.py --mode train --conf ./confs/directional_shadow.conf --case <case_name>_specular

On vertical-down shadow inputs

Extract vertical_down.zip and move each scene to ./public_data/nerf_synthetic. Then run

python exp_runner.py --mode train --conf ./confs/point_shadow.conf --case <case_name>_upup

On real data

Extract real_data.zip to ./public_data and run

python exp_runner.py --mode train --conf ./confs/real_data.conf --case <case_name>

On DeepShadow Dataset

You can download DeepShadowData.zip from their project page, and unzip it to ./public_data. Then run

python exp_runner.py --mode train --conf ./confs/deepshadow.conf --case <case_name>

Citation

Cite as below if you find this repository helpful:

@misc{ling2022shadowneus,
    title={ShadowNeuS: Neural SDF Reconstruction by Shadow Ray Supervision}, 
    author={Jingwang Ling and Zhibo Wang and Feng Xu},
    year={2022},
    eprint={2211.14086},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

Acknowledgement

The project structure is based on NeuS. Some code is borrowed from deep_shadow, IRON and psnerf. Thanks for these great projects.