  Recently, methods for neural surface representation and rendering, for
example NeuS, have shown that learning neural implicit surfaces through volume
rendering is becoming increasingly popular and making good progress. However,
these methods still face some challenges. Existing methods lack a direct
representation of depth information, which makes object reconstruction
unrestricted by geometric features, resulting in poor reconstruction of objects
with texture and color features. This is because existing methods only use
surface normals to represent implicit surfaces without using depth information.
Therefore, these methods cannot model the detailed surface features of objects
well. To address this problem, we propose a neural implicit surface learning
method called Depth-NeuS based on depth information optimization for multi-view
reconstruction. In this paper, we introduce depth loss to explicitly constrain
SDF regression and introduce geometric consistency loss to optimize for
low-texture areas. Specific experiments show that Depth-NeuS outperforms
existing technologies in multiple scenarios and achieves high-quality surface
reconstruction in multiple scenarios.
