One Shot Learning for Edge Detection on Point Clouds

and thus achieve superior results compared to networks that were trained on general data distributions. More specifically， by designing a filtered-KNN-based surface patch representation that supports a one-shot learning framework. Additionally， and its practical utility is validated by results across diverse real-scanned datasets， including indoor scenes like S3DIS dataset， 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。

which integrates Radial Basis Function-based Descriptor of the Surface patch。

we introduce an RBF_DoS module， by learning the specific data distribution of the target point cloud， we present a novel one-shot learning method allowing for edge extraction on point clouds，。

we present how to train a lightweight network named OSFENet (One-Shot edge Feature Extraction Network)， 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， and outdoor scenes such as the Semantic3D dataset and UrbanBIS dataset. 。