Preventing self-intersection with cycle regularization in neural networks for mesh reconstruction from a single RGB image

摘要

Self-intersection in surfaces is a typical defect that makes a 3D model unsuitable for many applications. Existing neural networks for 3D surface mesh reconstruction are faced with the challenge of integrating self-intersection prevention. In this paper, we propose a trainable cycle regularization in mesh reconstruction networks to prevent self-intersection. It is a general technique that can be easily implemented with existing surface mesh generation networks. Our experiments on two latest mesh reconstruction networks demonstrate that with the proposed cycle regularization, self-intersections in the generated meshes are significantly reduced, while the shape similarity is comparable with the original networks under the Chamfer distance metric. (C) 2019 Elsevier B.V. All rights reserved.