针对点云扫描仪差异，实现单次学习的边缘检测新方法。
One Shot Learning for Edge Detection on Point Clouds
- 基于滤波KNN构建表面块表示，支持单样本学习框架。
- 在ABC数据集上超越7个基线模型，实测表现优异。
- 适合小样本场景下的点云边缘提取，尤其适用于多源扫描数据。
每台扫描仪具有独特的特性及采样误差分布，若在包含多台扫描仪数据的通用数据集上训练网络，效果不如使用单一扫描仪数据训练。为此，本文提出一种新型单次学习方法，通过学习目标点云的特定数据分布，实现更优的边缘提取效果。具体而言，设计了一种基于滤波KNN的表面块表示，支持单次学习框架，并提出了RBF_DoS模块，利用基于径向基函数的表面块描述子，显著提升点云边缘检测性能。OSFENet在ABC数据集上与7个基线模型对比验证了优越性，同时在多种真实扫描数据集（包括S3DIS、Semantic3D和UrbanBIS）中展示了实际应用价值。
原文摘要 · Abstract (English)
Each scanner possesses its unique characteristics and exhibits its distinct sampling error distribution. Training a network on a dataset that includes data collected from different scanners is less effective than training it on data specific to a single scanner. Therefore, we present a novel one-shot learning method allowing for edge extraction on point clouds, by learning the specific data distribution of the target point cloud, and thus achieve superior results compared to networks that were trained on general data distributions. More specifically, we present how to train a lightweight network named OSFENet (One-Shot edge Feature Extraction Network), by designing a filtered-KNN-based surface patch representation that supports a one-shot learning framework. Additionally, we introduce an RBF_DoS module, which integrates Radial Basis Function-based Descriptor of the Surface patch, highly beneficial for the edge extraction on point clouds. The advantage of the proposed OSFENet is demonstrated through comparative analyses against 7 baselines on the ABC dataset, and its practical utility is validated by results across diverse real-scanned datasets, including indoor scenes like S3DIS dataset, and outdoor scenes such as the Semantic3D dataset and UrbanBIS dataset.
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