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This paper presents a novel framework to learn a concise geometric primitiverepresentation for 3D point clouds. Different from representing each type ofprimitive individually, we focus on the challenging problem of how to achieve aconcise and uniform representation robustly. We employ quadrics to representdiverse primitives with only 10 parameters and propose the first end-to-endlearning-based framework, namely QuadricsNet, to parse quadrics in pointclouds. The relationships between quadrics mathematical formulation andgeometric attributes, including the type, scale and pose, are insightfullyintegrated for effective supervision of QuaidricsNet. Besides, a novelpattern-comprehensive dataset with quadrics segments and objects is collectedfor training and evaluation. Experiments demonstrate the effectiveness of ourconcise representation and the robustness of QuadricsNet. Our code is availableat \url{https://github.com/MichaelWu99-lab/QuadricsNet}

QuadricsNet: Learning Concise Representation for Geometric Primitives in Point Clouds