Recovering Fine Details for Neural Implicit Surface Reconstruction
This work addresses a challenge in multi-view 3D reconstruction for computer vision applications, but it is incremental as it builds on an existing method.
The paper tackles the problem of recovering fine details in neural implicit surface reconstruction by introducing D-NeuS, which extends NeuS with two loss functions to reduce geometry bias and enforce multi-view feature consistency, resulting in high-accuracy surfaces that outperform state-of-the-art methods.
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.