Abstract
AbstractThis 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.
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