Neural Star Domain as Primitive Representation

Reconstructing 3D objects from 2D images is a fundamental task in computer vision, and an accurate structured reconstruction by parsimonious and semantic primitive representations has an even broader application range. When a target shape is reconstructed using multiple primitives, it is preferable that its fundamental properties, such as collective volume and surface, can be readily and comprehensively accessed so that the primitives can be treated as if they were a single shape. This becomes possible by a primitive representation with unified implicit and explicit representations. However, primitive representations in current approaches do not satisfy these requirements. To resolve this, we propose a novel primitive representation termed neural star domain (NSD) that learns primitive shapes in a star domain. We demonstrate that NSD is a universal approximator of the star domain; furthermore, it is not only parsimonious and semantic but also an implicit and explicit shape representation. The proposed approach outperforms existing methods in image reconstruction tasks in terms of semantic capability as well as sampling speed and quality for high-resolution meshes.