QuadricsNet: Learning Concise Representation for Geometric Primitives in Point Clouds
This addresses the challenge of representing diverse geometric primitives in point clouds for applications in computer vision and robotics, though it appears incremental as it builds on existing primitive representation methods.
The paper tackles the problem of learning a concise and uniform geometric primitive representation for 3D point clouds, using quadrics with only 10 parameters, and demonstrates effectiveness and robustness in experiments.
This paper presents a novel framework to learn a concise geometric primitive representation for 3D point clouds. Different from representing each type of primitive individually, we focus on the challenging problem of how to achieve a concise and uniform representation robustly. We employ quadrics to represent diverse primitives with only 10 parameters and propose the first end-to-end learning-based framework, namely QuadricsNet, to parse quadrics in point clouds. The relationships between quadrics mathematical formulation and geometric attributes, including the type, scale and pose, are insightfully integrated for effective supervision of QuaidricsNet. Besides, a novel pattern-comprehensive dataset with quadrics segments and objects is collected for training and evaluation. Experiments demonstrate the effectiveness of our concise representation and the robustness of QuadricsNet. Our code is available at \url{https://github.com/MichaelWu99-lab/QuadricsNet}