WildLight: In-the-wild Inverse Rendering with a Flashlight (CVPR 2023)

[Project page]|[Arxiv]

Dependencies

Conda is recommended for installing all dependencies

conda env create -f environ.yaml
conda activate wildlight

Data convention

Input data is organized in a single folder, where images are saved as exr/png files similar to NeuS style, OR packed within a single npy file

<case_name xxx>
|-- cameras_sphere.npz    # camera & lighting parameters

|-- images.npy
Or
|-- image
    |-- 000.exr        # target image for each view, either in exr or png format
    |-- 001.exr
    Or
    |-- 000.png        
    |-- 001.png
    ...
|-- mask [optional]
    |-- 000.png        # target mask each view, if available
    |-- 001.png
    ...

Camera and lighting parameters are stored in cameras_sphere.npz with following key strings:

• world_mat_x: $K_x[R_x|T_x]$ projection matrix from world coordinates to image coordinates
• scale_mat_x: Sim(3) transformation matrix from object coordinates to world coordinates; we will only recover shape & material inside a unit sphere ROI in object coordinates. Usually this matrix is static accross all views.
• light_energy_x: an RGB vector for flashlight intensity per view. If using a fixed power flashlight, this is set to $(1,1,1)$ for images under flashlight, or to $(0,0,0)$ for images without flashlight.
• max_intensity: [optional] a scalar indicating maximum pixel density (e.g. 255 for 8-bit images), defaults to inf

Config

Model and traning parameters are written into config files under confs/*.conf. We provide three configurations for our datasets: confs/synthetic.conf and confs/synthetic_maskless.conf for our synthetic data, and confs/real.conf for real data.

Running

1. Train. Run following line to download and train on the synthetic legocar object dataset. We provide a total of 7 objects: bunny, armadillo, legocar, plant (synthetic w/ ground turth) and bulldozer, cokecan and face (real scene, images only).

   python exp_runner.py --case legocar --conf confs/synthetic.conf --mode train --download_dataset
   

   Intermidiate results can be found under exp/legocar/masked/ folder.
2. Mesh and texture export.

   python exp_runner.py --case legocar --conf confs/synthetic.conf --mode validate_mesh --is_continue
   

   This will export a UV-unwraped OBJ file along with PBR texture maps from last checkpoint, under exp/legocar/masked/meshes/XXXXXXXX_export (this might take a few minutes.
3. Validate novel view rendering. A dataset_val must be provided in config.

   python exp_runner.py --case legocar --conf confs/synthetic.conf --mode validate_image --is_continue
   

   Results will be saved to exp/legocar/masked/novel_view/.

Results (rendered in blender)

https://user-images.githubusercontent.com/57708879/232410072-43d74df8-9438-4fc8-b302-0cd2c7f659ed.mp4

Acknowledgement

This repo is heavily built upon NeuS. We would like to thank the authors for opening source. Special thanks goes to @wei-mao-2019, a friend and fellow researcher who agreed to appear in our dataset.

BibTex

@article{cheng2023wildlight,
  title={WildLight: In-the-wild Inverse Rendering with a Flashlight},
  author={Cheng, Ziang and Li, Junxuan and Li, Hongdong},
  journal={arXiv preprint arXiv:2303.14190},
  year={2023}
}