Image-based detail reconstruction of non-Lambertian surfaces.

I.-Chen Lin; Wen-Hsing Chang; Yung-Sheng Lo; Jen-Yu Peng; Chan-Yu Lin

This paper presents a novel optimization framework for estimating the static or dynamic surfaces with details. The proposed method uses dense depths from a structured-light 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 (SFS) 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 approach can also estimate detailed undulations on textureless regions, and employs spatial-temporal constraints for reliably tracking time-varying surfaces. Experiment results demonstrate that accurate and detailed 3D surfaces can be reconstructed from images acquired by off-the-shelf devices. Copyright © 2010 John Wiley & Sons, Ltd.

COMPUTER-generated imagery; ANIMATION (Cinematography); GEOMETRY; MATHEMATICAL optimization; MATHEMATICS

Computer Animation & Virtual Worlds, 2010, Vol 21, Issue 1, p55

1546-4261

Academic Journal

10.1002/cav.332