Authors: Ziyu Tang, Weicai Ye, Yifan Wang, Di Huang, Hujun Bao, Tong He, Guofeng Zhang
Abstract: 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.
Source: http://arxiv.org/abs/2408.12598v1
