Semi-signed neural fitting for surface reconstruction from unoriented point clouds

• URL: http://arxiv.org/abs/2206.06715v1
• Date: Tue, 14 Jun 2022 09:40:17 GMT
• Title: Semi-signed neural fitting for surface reconstruction from unoriented point clouds
• Authors: Runsong Zhu, Di Kang, Ka-Hei Hui, Yue Qian, Xuefei Zhe, Zhen Dong, Linchao Bao, Chi-Wing Fu
• Abstract summary: We propose SSN-Fitting to reconstruct a better signed distance field. SSN-Fitting consists of a semi-signed supervision and a loss-based region sampling strategy. We conduct experiments to demonstrate that SSN-Fitting achieves state-of-the-art performance under different settings.
• Score: 53.379712818791894
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Reconstructing 3D geometry from \emph{unoriented} point clouds can benefit many downstream tasks. Recent methods mostly adopt a neural shape representation with a neural network to represent a signed distance field and fit the point cloud with an unsigned supervision. However, we observe that using unsigned supervision may cause severe ambiguities and often leads to \emph{unexpected} failures such as generating undesired surfaces in free space when reconstructing complex structures and struggle with reconstructing accurate surfaces. To reconstruct a better signed distance field, we propose semi-signed neural fitting (SSN-Fitting), which consists of a semi-signed supervision and a loss-based region sampling strategy. Our key insight is that signed supervision is more informative and regions that are obviously outside the object can be easily determined. Meanwhile, a novel importance sampling is proposed to accelerate the optimization and better reconstruct the fine details. Specifically, we voxelize and partition the object space into \emph{sign-known} and \emph{sign-uncertain} regions, in which different supervisions are applied. Also, we adaptively adjust the sampling rate of each voxel according to the tracked reconstruction loss, so that the network can focus more on the complex under-fitting regions. We conduct extensive experiments to demonstrate that SSN-Fitting achieves state-of-the-art performance under different settings on multiple datasets, including clean, density-varying, and noisy data.

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