CanonNet: Canonical Ordering and Curvature Learning for Point Cloud Analysis

• URL: http://arxiv.org/abs/2504.02763v1
• Date: Thu, 03 Apr 2025 16:58:57 GMT
• Title: CanonNet: Canonical Ordering and Curvature Learning for Point Cloud Analysis
• Authors: Benjy Friedmann, Michael Werman,
• Abstract summary: CanonNet is a lightweight neural network composed of two complementary components.<n>We present CanonNet, a preprocessing pipeline that creates a canonical point ordering and orientation.<n>We also present a geometric learning framework where networks learn from synthetic surfaces with precise curvature values.
• Score: 0.5919433278490629
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Point cloud processing poses two fundamental challenges: establishing consistent point ordering and effectively learning fine-grained geometric features. Current architectures rely on complex operations that limit expressivity while struggling to capture detailed surface geometry. We present CanonNet, a lightweight neural network composed of two complementary components: (1) a preprocessing pipeline that creates a canonical point ordering and orientation, and (2) a geometric learning framework where networks learn from synthetic surfaces with precise curvature values. This modular approach eliminates the need for complex transformation-invariant architectures while effectively capturing local geometric properties. Our experiments demonstrate state-of-the-art performance in curvature estimation and competitive results in geometric descriptor tasks with significantly fewer parameters (\textbf{100X}) than comparable methods. CanonNet's efficiency makes it particularly suitable for real-world applications where computational resources are limited, demonstrating that mathematical preprocessing can effectively complement neural architectures for point cloud analysis. The code for the project is publicly available \hyperlink{https://benjyfri.github.io/CanonNet/}{https://benjyfri.github.io/CanonNet/}.

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