Surface Reconstruction From Normals: A Robust DGP-Based Discontinuity Preservation Approach

Wuyuan Xie, Miaohui Wang, Mingqiang Wei, Jianmin Jiang, Jing Qin; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 5328-5336

Abstract


In 3D surface reconstruction from normals, discontinuity preservation is an important but challenging task. However, existing studies fail to address the discontinuous normal maps by enforcing the surface integrability in the continuous domain. This paper introduces a robust approach to preserve the surface discontinuity in the discrete geometry way. Firstly, we design two representative normal incompatibility features and propose an efficient discontinuity detection scheme to determine the splitting pattern for a discrete mesh. Secondly, we model the discontinuity preservation problem as a light-weight energy optimization framework by jointly considering the discontinuity detection and the overall reconstruction error. Lastly, we further shrink the feasible solution space to reduce the complexity based on the prior knowledge. Experiments show that the proposed method achieves the best performance on an extensive 3D dataset compared with the state-of-the-arts in terms of mean angular error and computational complexity.

Related Material


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[bibtex]
@InProceedings{Xie_2019_CVPR,
author = {Xie, Wuyuan and Wang, Miaohui and Wei, Mingqiang and Jiang, Jianmin and Qin, Jing},
title = {Surface Reconstruction From Normals: A Robust DGP-Based Discontinuity Preservation Approach},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}