Image-based detail reconstruction of non-Lambertian surfaces

This paper presents a novel optimization framework for estimating the static or dynamic surfaces with details. The proposed method uses dense depths from a structuredlight system or sparse ones from motion capture as the initial positions, and exploits non-Lambertian reflectance models to approximate surface reflectance. Multi-stage shape-from-shading is then applied to optimize both shape geometry and reflectance properties. Because this method uses non-Lambertian properties, it can compensate for triangulation reconstruction errors caused by view-dependent reflections. This ap1 

• Type: Journal
• Year: 2010
• Where: JVCA
• Authors: I-Chen Lin, Wen-Hsing Chang, Yung-Sheng Lo, Jen-Yu Peng, Chan-Yu Lin
• Keywords: Dense Depths, JVCA 2010, Non-Lambertian Reflectance Models, Triangulation Reconstruction Errors