@inproceedings{d48327d9f1b649a08d5fc563944b4159,
title = "L-DPSNet: Deep Photometric Stereo Network via Local Diffuse Reflectance Maxima",
abstract = "Reconstructing surface normal from the reflectance observations of real objects is a challenging issue. Although recent works on photometric stereo exploit various reflectance-normal mapping models, none of them take both illumination and LDR maximum into account. In this paper, we combine a fusion learning network with LDR maxima to recover the normal of the underlying surface. Unlike traditional formalization, the initial normal estimated by solving the generalized bas-relief (GBR) ambiguity is employed to promote the performance of our learning framework. As an uncalibrated photometric stereo network, our method, called L-DPSNet, takes advantage of LDR-derived information in normal prediction. We present the qualitative and quantitative experiments implemented using synthetic and real data to demonstrate the effectiveness of the proposed model.",
keywords = "Deep neural network, Diffuse reflectance maxima, Uncalibrated photometric stereo",
author = "Kanghui Zeng and Chao Xu and Jing Hu and Yushi Li and Zhaopeng Meng",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 28th International Conference on Neural Information Processing, ICONIP 2021 ; Conference date: 08-12-2021 Through 12-12-2021",
year = "2021",
doi = "10.1007/978-3-030-92307-5_11",
language = "English",
isbn = "9783030923068",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "89--97",
editor = "Teddy Mantoro and Minho Lee and Ayu, {Media Anugerah} and Wong, {Kok Wai} and Hidayanto, {Achmad Nizar}",
booktitle = "Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings",
}