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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