We introduce anchored radial observations (ARO), a novel shape encoding for learning implicit field representation of 3D shapes that is category-agnostic and generalizable amid significant shape variations. The main idea behind our work is to reason about shapes through partial observations from a set of viewpoints, called anchors. We develop a general and unified shape representation by employing a fixed set of anchors, via Fibonacci sampling, and designing a coordinate-based deep neural network to predict the occupancy value of a query point in space. Differently from prior neural implicit models that use global shape feature, our shape encoder operates on contextual, query-specific features. To predict point occupancy, locally observed shape information from the perspective of the anchors surrounding the input query point are encoded and aggregated through an attention module, before implicit decoding is performed. We demonstrate the quality and generality of our network, coined ARO-Net, on surface reconstruction from sparse point clouds, with tests on novel and unseen object categories, "one-shape" training, and comparisons to state-of-the-art neural and classical methods for reconstruction and tessellation.

翻译：我们提出了锚定径向观测（ARO）——一种新颖的形状编码方法，用于学习3D形状的隐式场表示，该方法具有类别无关性，并能应对显著形状变化。本工作的核心思想是通过一组名为锚点的视角进行局部观测来推理形状。通过利用斐波那契采样固定锚点集，并设计基于坐标的深度神经网络预测空间查询点的占据值，我们构建了通用统一的形状表示。与依赖全局形状特征的先前神经隐式模型不同，我们的形状编码器基于上下文、查询特定特征进行操作。为预测点占据状态，通过注意力模块对输入查询点周围锚点视角下的局部观测形状信息进行编码与聚合，随后执行隐式解码。我们通过稀疏点云表面重建实验展示了所提网络（称为ARO-Net）的质量与泛化能力，包括对未见目标类别的测试、单形状训练，以及与最先进的神经与传统重建及曲面细分方法的比较。