Recent advances in localized implicit functions have enabled neural implicit representation to be scalable to large scenes. However, the regular subdivision of 3D space employed by these approaches fails to take into account the sparsity of the surface occupancy and the varying granularities of geometric details. As a result, its memory footprint grows cubically with the input volume, leading to a prohibitive computational cost even at a moderately dense decomposition. In this work, we present a learnable hierarchical implicit representation for 3D surfaces, coded OctField, that allows high-precision encoding of intricate surfaces with low memory and computational budget. The key to our approach is an adaptive decomposition of 3D scenes that only distributes local implicit functions around the surface of interest. We achieve this goal by introducing a hierarchical octree structure to adaptively subdivide the 3D space according to the surface occupancy and the richness of part geometry. As octree is discrete and non-differentiable, we further propose a novel hierarchical network that models the subdivision of octree cells as a probabilistic process and recursively encodes and decodes both octree structure and surface geometry in a differentiable manner. We demonstrate the value of OctField for a range of shape modeling and reconstruction tasks, showing superiority over alternative approaches.
翻译:局部隐含功能的最近进展使得神经隐含的表达方式能够向大场景伸缩。然而,这些方法所使用的3D空间的常规子剖面没有考虑到地表占用的宽度和几何细节的不同颗粒。因此,其记忆足迹随着输入量的相隔增长,甚至根据地表占用程度和部分地貌的丰富性,造成令人望而生畏的计算成本。在这项工作中,我们为3D表面(代号为 " 10点 " )提供了一个可学的等级隐含的表示方式,允许高精度地层的复杂表面和低记忆和计算预算。我们的方法的关键是将仅将本地隐含的功能分布在利益表层周围的3D场景进行适应性分解。我们通过引入一个等级的奥氏结构,根据地表占用程度和部分地貌的丰富性分解,将3D空间按适应性地分解成一个令人窒息的计算成本。我们进一步提议建立一个新型的分层网络,以替代的代形细胞的代位结构为可比较性过程和递制的地平面结构,展示了一种可变的地形结构和代地貌结构。