  Recent works on implicit neural representations have made significant
strides. Learning implicit neural surfaces using volume rendering has gained
popularity in multi-view reconstruction without 3D supervision. However,
accurately recovering fine details is still challenging, due to the underlying
ambiguity of geometry and appearance representation. In this paper, we present
D-NeuS, a volume rendering-base neural implicit surface reconstruction method
capable to recover fine geometry details, which extends NeuS by two additional
loss functions targeting enhanced reconstruction quality. First, we encourage
the rendered surface points from alpha compositing to have zero signed distance
values, alleviating the geometry bias arising from transforming SDF to density
for volume rendering. Second, we impose multi-view feature consistency on the
surface points, derived by interpolating SDF zero-crossings from sampled points
along rays. Extensive quantitative and qualitative results demonstrate that our
method reconstructs high-accuracy surfaces with details, and outperforms the
state of the art.
