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’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.
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Acknowledgement
This work was supported by the National Key Technology Support Program under Grant 2012BAH64F01 and 2013BAH48F01 and National Natural Science Foundation of China 61370134. The authors would like to thank Wojciech Matusik et al. [2] for using their measured BRDF data.
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Hao, J., Liu, Y., Weng, D. (2015). A BRDF Representing Method Based on Gaussian Process. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9009. Springer, Cham. https://doi.org/10.1007/978-3-319-16631-5_40
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DOI: https://doi.org/10.1007/978-3-319-16631-5_40
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