Neural implicit reconstruction via volume rendering has demonstrated its effectiveness in recovering dense 3D surfaces. However, it is non-trivial to simultaneously recover meticulous geometry and preserve smoothness across regions with differing characteristics. To address this issue, previous methods typically employ geometric priors, which are often constrained by the performance of the prior models. In this paper, we propose ND-SDF, which learns a Normal Ddeflection field to represent the angular deviation between the scene normal and the prior normal. Unlike previous methods that uniformly apply geometric priors on all samples, introducing significant bias in accuracy, our proposed normal deflection field dynamically learns and adapts the utilization of samples based on their specific characteristics, thereby improving both the accuracy and effectiveness of the model. Our method not only obtains smooth weakly textured regions such as walls and floors but also preserves the geometric details of complex structures. In addition, we introduce a novel ray sampling strategy based on the deflection angle to facilitate the unbiased rendering process, which significantly improves the quality and accuracy of intricate surfaces, especially on thin structures. Consistent improvements on various challenging datasets demonstrate the superiority of our method.

翻译：基于神经隐式表示与体渲染的三维重建方法已证明其在恢复稠密三维表面方面的有效性。然而，如何同时恢复精细几何结构并在具有不同特征的区域间保持平滑性仍具挑战性。为解决此问题，现有方法通常引入几何先验，但其性能往往受限于先验模型的表现。本文提出ND-SDF，通过学习法向偏转场来表征场景法向与先验法向之间的角度偏差。不同于以往方法对所有采样点统一施加几何先验（这会引入显著的精度偏差），我们提出的法向偏转场能够根据采样点的具体特征动态学习并调整其使用方式，从而提升模型的精度与有效性。我们的方法不仅能获得平滑的弱纹理区域（如墙壁和地板），还能保持复杂结构的几何细节。此外，我们提出一种基于偏转角的新型光线采样策略，以促进无偏渲染过程，显著提升了复杂表面（尤其是薄壁结构）的重建质量与精度。在多个挑战性数据集上的持续改进证明了本方法的优越性。