Abstract

Reconstructing the shape of a 3D object from multi-view images under unknown,general illumination is a fundamental prohlem in computer vision and high quality reconstruction is usually challenging especially when high detail is needed.This paper presents a total variation(TV)based approach for recovering surface details using shading and multi-view stereo(MVS).Behind the approach are our two important observations:(1)the il lumination over the surface of an object tends to be piecewise smooth and(2)the recovery of surface orientation is not sufficicnt for reconstructing gcometry.which were previously overlooked.Thus we introduce TV to regularize the lighting and use visual hull to constrain partial vertices.The reconstruction is formulated as a constrained TV-minimization prblem that treats the shape and lighting as unknowns simultaneously.An augmented Lagrangian method is proposed to quickly solve the TV-minimization problem.As a result,our approach is robust,stable and is able to efficicntly recover high quality of surface details even starting with a coarse MVS.These advantages are demonstrated by the experiments with synthetie and real world examples