  Recent works on implicit neural representations have shown promising results
for multi-view surface reconstruction. However, most approaches are limited to
relatively simple geometries and usually require clean object masks for
reconstructing complex and concave objects. In this work, we introduce a novel
neural surface reconstruction framework that leverages the knowledge of stereo
matching and feature consistency to optimize the implicit surface
representation. More specifically, we apply a signed distance field (SDF) and a
surface light field to represent the scene geometry and appearance
respectively. The SDF is directly supervised by geometry from stereo matching,
and is refined by optimizing the multi-view feature consistency and the
fidelity of rendered images. Our method is able to improve the robustness of
geometry estimation and support reconstruction of complex scene topologies.
Extensive experiments have been conducted on DTU, EPFL and Tanks and Temples
datasets. Compared to previous state-of-the-art methods, our method achieves
better mesh reconstruction in wide open scenes without masks as input.
