@inproceedings{0b89a58d279e40318dca5944787c316e,
title = "A BRDF representing method based on Gaussian process",
abstract = "In recent years, digital reconstruction of cultural heritage provides an effective way of protecting historical relics, in which the modeling of surface reflection of historical heritage with high fidelity places a very important role. In this paper Gaussian process (GP) regression based approach is proposed to model the reflection properties of real materials, in which the simulation data generated by the existing model are both used as the training data and the proof that Gaussian process model can be used to describe the material reflection. Matusik{\textquoteright}s MERL database is also adopted to perform training and inference and obtain the reflection model of the real material. Simulation results show that the proposed GP regression approach can achieve a good fitting of the reflection properties of certain materials, greatly reduce the BRDF measurement time and ensure high realistic rendering at the same time.",
author = "Jianying Hao and Yue Liu and Dongdong Weng",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 12th Asian Conference on Computer Vision, ACCV 2014 ; Conference date: 01-11-2014 Through 05-11-2014",
year = "2015",
doi = "10.1007/978-3-319-16631-5\_40",
language = "English",
isbn = "9783319166308",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "542--553",
editor = "C.V. Jawahar and Shiguang Shan",
booktitle = "Computer Vision - ACCV 2014 Workshops, Revised Selected Papers",
address = "Germany",
}