Overview

This repository provides an official implementation¹ for our paper CVPR 2021 "Multi-view 3D Reconstruction of a Texture-less Smooth Surface of Unknown Generic Reflectance" - a multiview photometric pipeline for reconstructing glossy objects from a few calibrated multi-view images.

We assume the objects to be reconstructed are made from uniform material, and are illuminated by a single point light source that is closely attached to a moving camera (e.g. smartphones with a built-in flash light). The input to our system is a set of images captured by the camera, and the output is an oriented point cloud (3D + normal directions) that corresponds to the surface of the object, as well as its BRDF estimation.

There is no machine learning component in this pipeline. Instead, we base our solution solely on an energy model and design a robust optimisation technique for solving it! For more details please refer to our paper.

Please consider citing our paper if you find this implementation useful.

@inproceedings{cheng2021multi,
  title={Multi-view 3D Reconstruction of a Texture-less Smooth Surface of Unknown Generic Reflectance},
  author={Cheng, Ziang and Li, Hongdong and Asano, Yuta and Zheng, Yinqiang and Sato, Imari},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={16226--16235},
  year={2021}
}

Dependencies and requirement

This implementation requqires an NVIDIA GPU for parallel computing. You will need about 2.3GB free VRAM for running the demo (memory requirement linearly increases with number of images and resolution). A conda virtual environment is recommended for managing packages. Starting with a fresh conda venv with Python3, install following packages:

• opencv-python

   pip install opencv-python --user
  

• pytorch with CUDA support (tested w/ v1.7.0)

  conda install pytorch torchvision torchaudio cudatoolkit -c pytorch
  

• trimesh with pyembree support

  conda install -c conda-forge pyembree
  pip install trimesh --user
  

• matplotlib, scipy, tqdm, h5py, sklearn

  pip install numpy scipy matplotlib tqdm h5py scikit-learn --user
  

Clone this repository

git clone https://github.com/za-cheng/PM-PMVS.git
cd PM-PMVS
conda activate VENV_NAME # insert virtual environment name

Prepare input files

Before running the pipeline you need to create an input file that contains multiview images and camera parameters. An exmaple script steel-bunny.py is included showing how to do this. In this example a steel bunny model is rendered at 10 viewpoints:

python steel-bunny.py

Running above command should create an input file at data/steel-bunny.npy. It should also save rendered images to results/input-XX.png, though these images won't be needed since they are already packed in the input file.

(They will look something like this:)

Parameter configurations are supplied via params.py with instructions given inside. You will probably want to modify this script as well before running the pipeline on your own images.

Run the pipeline

Simply run the pipeline with following commands:

python main.py

Intermediate results will be saved to results/ folder as each iteration completes.

License

The software is free for academic purposes. For commercial use please Email us.

Footnotes

1. Thanks to a covid lockdown here in Canberra I havn't beed able to access my office in university. As a result some script in repo is rewritten from a much orlder version and others from scratch. With that said, the results should be consistent with what's been presented in paper. ↩