Implicit Neural Representations have gained prominence as a powerful framework for capturing complex data modalities, encompassing a wide range from 3D shapes to images and audio. Within the realm of 3D shape representation, Neural Signed Distance Functions (SDF) have demonstrated remarkable potential in faithfully encoding intricate shape geometry. However, learning SDFs from sparse 3D point clouds in the absence of ground truth supervision remains a very challenging task. While recent methods rely on smoothness priors to regularize the learning, our method introduces a regularization term that leverages adversarial samples around the shape to improve the learned SDFs. Through extensive experiments and evaluations, we illustrate the efficacy of our proposed method, highlighting its capacity to improve SDF learning with respect to baselines and the state-of-the-art using synthetic and real data.

本文解决了在缺乏真实监督的情况下，从稀疏3D点云中学习神经有符号距离函数（SDF）所面临的挑战。我们提出了一种利用形状周围对抗样本的正则化方法，显著提升了SDF学习的效果。实验结果表明，所提方法在合成数据和真实数据中均优于现有基线和最新成果。

少样本无监督隐式神经形状表示学习与空间对抗