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 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.