Learning Unsupervised Hierarchical Part Decomposition of 3D Objects from a Single RGB Image

• URL: http://arxiv.org/abs/2004.01176v1
• Date: Thu, 2 Apr 2020 17:58:05 GMT
• Title: Learning Unsupervised Hierarchical Part Decomposition of 3D Objects from a Single RGB Image
• Authors: Despoina Paschalidou, Luc van Gool, and Andreas Geiger
• Abstract summary: We propose a novel formulation that allows to jointly recover the geometry of a 3D object as a set of primitives. Our model recovers the higher level structural decomposition of various objects in the form of a binary tree of primitives. Our experiments on the ShapeNet and D-FAUST datasets demonstrate that considering the organization of parts indeed facilitates reasoning about 3D geometry.
• Score: 102.44347847154867
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Humans perceive the 3D world as a set of distinct objects that are characterized by various low-level (geometry, reflectance) and high-level (connectivity, adjacency, symmetry) properties. Recent methods based on convolutional neural networks (CNNs) demonstrated impressive progress in 3D reconstruction, even when using a single 2D image as input. However, the majority of these methods focuses on recovering the local 3D geometry of an object without considering its part-based decomposition or relations between parts. We address this challenging problem by proposing a novel formulation that allows to jointly recover the geometry of a 3D object as a set of primitives as well as their latent hierarchical structure without part-level supervision. Our model recovers the higher level structural decomposition of various objects in the form of a binary tree of primitives, where simple parts are represented with fewer primitives and more complex parts are modeled with more components. Our experiments on the ShapeNet and D-FAUST datasets demonstrate that considering the organization of parts indeed facilitates reasoning about 3D geometry.

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