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|Title:||Shading-based surface detail recovery under general unknown illumination||Authors:||Xu, Di
|Keywords:||Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision||Issue Date:||2018||Source:||Xu, D., Duan, Q., Zheng, J., Zhang, J., Cai, J., & Cham, T.-J. (2018). Shading-based surface detail recovery under general unknown illumination. IEEE transactions on pattern analysis and machine intelligence, 40(2), 423-436. doi:10.1109/TPAMI.2017.2671458||Journal:||IEEE transactions on pattern analysis and machine intelligence||Abstract:||Reconstructing the shape of a 3D object from multi-view images under unknown, general illumination is a fundamental problem in computer vision. High quality reconstruction is usually challenging especially when fine detail is needed and the albedo of the object is non-uniform. This paper introduces vertex overall illumination vectors to model the illumination effect and presents a total variation (TV) based approach for recovering surface details using shading and multi-view stereo (MVS). Behind the approach are the two important observations: (1) the illumination over the surface of an object often appears to be piecewise smooth and (2) the recovery of surface orientation is not sufficient for reconstructing the surface, which was often overlooked previously. Thus we propose to use TV to regularize the overall illumination vectors and use visual hull to constrain partial vertices. The reconstruction is formulated as a constrained TV-minimization problem that simultaneously treats the shape and illumination vectors as unknowns. 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 efficiently recover high-quality surface details even when starting with a coarse model obtained using MVS. These advantages are demonstrated by extensive experiments on the state-of-the-art MVS database, which includes challenging objects with varying albedo.||URI:||https://hdl.handle.net/10356/138217||ISSN:||0162-8828||DOI:||10.1109/TPAMI.2017.2671458||Rights:||© 2017 IEEE. All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||IMI Journal Articles|
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