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About this paper
Wenzheng Chen, Jun Gao, Huan Ling, Edward J. Smith, Jaakko Lehtinen, Alec Jacobson, Sanja Fidler
DIB-R enables image losses to train 3D meshes from 2D supervision. It interpolates attributes from the nearest visible face at covered pixels and aggregates distance-based face signals at background pixels, providing shape-learning feedback. On 13 ShapeNet categories, it reaches mean 3D IoU of 61.2%, versus 59.3% for SoftRas-Mesh; this whole-renderer comparison is not a win in every category.
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