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:我们引入了锚定径向观测(ARO),这是一种新颖的形状编码方法,用于学习三维形状的隐式场表示,是类别不可知的,并且在显著的形状变化中具有通用性。我们的工作背后的主要思想是通过一组称为锚点的视点的部分观测来推断形状。通过使用一组固定的锚定点(通过斐波那契采样获得),我们开发了一种通用的形状表示,并设计了一个基于坐标的深度神经网络来预测空间中查询点的占据值。与使用全局形状特征的先前神经隐式模型不同,我们的形状编码器在上下文查询特征上操作。为了预测点的占据情况,在执行隐式解码之前,必须通过关注模块对围绕输入查询点的锚点视角下观察到的局部形状信息进行编码和聚合。我们证明了我们的网络ARO-Net在稀疏点云的表面重建上的质量和通用性,进行了新颖和未见过的对象类别的测试,“一个形状”的训练,并与用于重建和镶嵌的最先进的神经和经典方法进行了比较。