State-of-the-art neural implicit surface representations have achieved
impressive results in indoor scene reconstruction by incorporating monocular
geometric priors as additional supervision. However, we have observed that
multi-view inconsistency between such priors poses a challenge for high-quality
reconstructions. In response, we present NC-SDF, a neural signed distance field
(SDF) 3D reconstruction framework with view-dependent normal compensation (NC).
Specifically, we integrate view-dependent biases in monocular normal priors
into the neural implicit representation of the scene. By adaptively learning
and correcting the biases, our NC-SDF effectively mitigates the adverse impact
of inconsistent supervision, enhancing both the global consistency and local
details in the reconstructions. To further refine the details, we introduce an
informative pixel sampling strategy to pay more attention to intricate geometry
with higher information content. Additionally, we design a hybrid geometry
modeling approach to improve the neural implicit representation. Experiments on
synthetic and real-world datasets demonstrate that NC-SDF outperforms existing
approaches in terms of reconstruction quality.

基于神经隐式表面表示的 3D 重建框架 NC-SDF，通过视角缺陷的学习和修正，提高了重建的全局一致性和局部细节，并通过信息丰富的像素采样策略和混合几何建模方法进一步改善了重建质量。