Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstruction

• URL: http://arxiv.org/abs/2205.15848v1
• Date: Tue, 31 May 2022 14:52:07 GMT
• Title: Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstruction
• Authors: Qiancheng Fu, Qingshan Xu, Yew-Soon Ong, Wenbing Tao
• Abstract summary: We propose geometry-consistent neural implicit surfaces learning for multi-view reconstruction. Our proposed method achieves high-quality surface reconstruction in both complex thin structures and large smooth regions.
• Score: 41.43563122590449
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, neural implicit surfaces learning by volume rendering has become popular for multi-view reconstruction. However, one key challenge remains: existing approaches lack explicit multi-view geometry constraints, hence usually fail to generate geometry consistent surface reconstruction. To address this challenge, we propose geometry-consistent neural implicit surfaces learning for multi-view reconstruction. We theoretically analyze that there exists a gap between the volume rendering integral and point-based signed distance function (SDF) modeling. To bridge this gap, we directly locate the zero-level set of SDF networks and explicitly perform multi-view geometry optimization by leveraging the sparse geometry from structure from motion (SFM) and photometric consistency in multi-view stereo. This makes our SDF optimization unbiased and allows the multi-view geometry constraints to focus on the true surface optimization. Extensive experiments show that our proposed method achieves high-quality surface reconstruction in both complex thin structures and large smooth regions, thus outperforming the state-of-the-arts by a large margin.

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