CanonNet: Canonical Ordering and Curvature Learning for Point Cloud Analysis
This work addresses point cloud analysis for applications like 3D modeling and robotics, offering an efficient solution for resource-limited settings, but it is incremental as it builds on existing preprocessing and learning methods.
The paper tackled the challenges of inconsistent point ordering and learning fine-grained geometric features in point cloud processing by introducing CanonNet, a lightweight neural network with a canonical ordering preprocessing pipeline and a geometric learning framework using synthetic surfaces, achieving state-of-the-art performance in curvature estimation and competitive results in geometric descriptor tasks with 100X fewer parameters.
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/}.