GUARD: Geometric Uncertainty-Aware Point Cloud Denoising and Segmentation
Overview
GUARD improves robotic disassembly by distinguishing real geometry from scanning artifacts using a geometric uncertainty-aware framework with a multi-scale transformer and Gaussian Process, outperforming predictive entropy in detecting corrupted points.
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- arXiv Robotics — research abstractsGUARD: Geometric Uncertainty-Aware Point Cloud Denoising and Segmentation for Robotic Hard Disk Drive Disassembly
GUARD is a geometric uncertainty-aware framework that improves point cloud segmentation for robotic disassembly by distinguishing between genuine component geometry and scanning artifacts. It combines a multi-scale geometric transformer with a Gaussian Process to estimate per-point uncertainty, achieving better performance than predictive entropy in detecting corrupted points.
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