TY - GEN
T1 - A BRDF representing method based on Gaussian process
AU - Hao, Jianying
AU - Liu, Yue
AU - Weng, Dongdong
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - 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’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.
AB - 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’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.
UR - http://www.scopus.com/inward/record.url?scp=84942549207&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-16631-5_40
DO - 10.1007/978-3-319-16631-5_40
M3 - Conference contribution
AN - SCOPUS:84942549207
SN - 9783319166308
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 542
EP - 553
BT - Computer Vision - ACCV 2014 Workshops, Revised Selected Papers
A2 - Jawahar, C.V.
A2 - Shan, Shiguang
PB - Springer Verlag
T2 - 12th Asian Conference on Computer Vision, ACCV 2014
Y2 - 1 November 2014 through 5 November 2014
ER -