  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.
