Abstract
Extracting geometric edges from unstructured point clouds remains a significant challenge, particularly in thin-walled structures that are commonly found in everyday objects. Traditional geometric methods and recent learning-based approaches frequently struggle with these structures, as both rely heavily on sufficient contextual information from local point neighborhoods. However, 3D measurement data of thin-walled structures often lack the accurate, dense, and regular neighborhood sampling required for reliable edge extraction, resulting in degraded performance. In this work, we introduce STAR-Edge, a novel approach designed for detecting and refining edge points in thin-walled structures. Our method leverages a unique representation-the local spherical curve-to create structure-aware neighborhoods that emphasize co-planar points while reducing interference from close-by, non-co-planar surfaces. This representation is transformed into a rotation-invariant descriptor, which, combined with a lightweight multi-layer perceptron, enables robust edge point classification even in the presence of noise and sparse or irregular sampling. Besides, we also use the local spherical curve representation to estimate more precise normals and introduce an optimization function to project initially identified edge points exactly on the true edges. Experiments conducted on the ABC dataset and thin-walled structure-specific datasets demonstrate that STAR-Edge outperforms existing edge detection methods, showcasing better robustness under various challenging conditions.
Abstract (translated)
从非结构化点云中提取几何边缘仍然是一个重大挑战，尤其是在日常物体中常见的薄壁结构。传统几何方法和近期基于学习的方法在处理这些结构时经常遇到困难，因为它们都严重依赖于局部点邻域提供的充足上下文信息。然而，由于3D测量数据中的薄壁结构通常缺乏精确、密集且规则的局部采样，这导致了可靠的边缘提取性能下降。 为此，我们提出了一种名为STAR-Edge的新方法，专门用于检测和细化薄壁结构中的边缘点。该方法利用一种独特的表示方式——局部球面曲线（local spherical curve），以此来创建具有结构意识的邻域，突出共面点的同时减少与非共平面表面带来的干扰。此表示进一步被转换为旋转不变描述符，并结合轻量级多层感知器，即使在存在噪声和稀疏或不规则采样情况下也能实现鲁棒边缘点分类。 此外，我们还利用局部球面曲线来估计更精确的法线，并引入优化函数将初始识别的边缘点准确投影到真正的边缘上。实验结果显示，在ABC数据集以及针对薄壁结构专门的数据集中，STAR-Edge在各种挑战性条件下均优于现有的边缘检测方法，表现出更强的鲁棒性。
URL
https://arxiv.org/abs/2503.00801
