Neural implicits have become popular for representing surfaces because they offer an adaptive resolution and support arbitrary topologies. While previous works rely on ground truth point clouds, they often ignore the effect of input quality and sampling methods during reconstructing process. In this paper, we introduce a sampling method with an uncertainty-augmented surface implicit representation that employs a sampling technique that considers the geometric characteristics of inputs. To this end, we introduce a strategy that efficiently computes differentiable geometric features, namely, mean curvatures, to augment the sampling phase during the training period. The uncertainty augmentation offers insights into the occupancy and reliability of the output signed distance value, thereby expanding representation capabilities into open surfaces. Finally, we demonstrate that our method leads to state-of-the-art reconstructions on both synthetic and real-world data.

翻译：神经隐式函数因具有自适应分辨率并支持任意拓扑结构，已成为表示表面的常用方法。以往研究依赖真实点云数据，但往往忽略重建过程中输入质量与采样方法的影响。本文提出一种结合不确定性增强表面隐式表示的采样方法，该方法在采样过程中考虑了输入的几何特征。为此，我们引入一种策略，高效计算可微几何特征（即平均曲率），以在训练阶段增强采样过程。不确定性增强机制可提供输出有符号距离值的占据状态与可靠性信息，从而将表示能力扩展至开放表面。最后，我们证明该方法在合成数据和真实数据上均能达到最先进的重建效果。