We present a novel framework to learn to convert the perpixel photometric information at each view into spatially distinctive and view-invariant low-level features, which can be plugged into existing multi-view stereo pipeline for enhanced 3D reconstruction. Both the illumination conditions during acquisition and the subsequent per-pixel feature transform can be jointly optimized in a differentiable fashion. Our framework automatically adapts to and makes efficient use of the geometric information available in different forms of input data. High-quality 3D reconstructions of a variety of challenging objects are demonstrated on the data captured with an illumination multiplexing device, as well as a point light. Our results compare favorably with state-of-the-art techniques.
@article{arxiv.2103.14794,
title = {Learning Efficient Photometric Feature Transform for Multi-view Stereo},
author = {Kaizhang Kang and Cihui Xie and Ruisheng Zhu and Xiaohe Ma and Ping Tan and Hongzhi Wu and Kun Zhou},
journal= {arXiv preprint arXiv:2103.14794},
year = {2021}
}