Feature Extraction from Space Debris Point Clouds with Geometry-Guided Contour Extraction and Multilayer Perceptron

Feature extraction of space debris from point cloud data faces challenges such as difficulty in contour extraction, structural complexity, and strong noise interference. To address these challenges, we propose the Space Debris Point Cloud Feature Extraction Network (SFENet), which combines geometry-guided contour extraction with a multilayer perceptron (MLP). First, a geometry-guided contour extraction procedure analyzes local neighborhoods of the three-dimensional point cloud. Spherical neighborhoods are divided into angular sectors, and the distribution of neighboring points is evaluated to identify sparse or empty regions associated with candidate contour points. The resulting contour candidates are then processed by the MLP-based feature learning network to obtain discriminative local and global representations. Second, in the feature learning stage, a multilayer perceptron network is constructed. Through hierarchical abstraction and multi-scale feature fusion, the point cloud is effectively extracted. Finally, multiview point cloud registration and classification experiments are conducted to evaluate the geometric quality, robustness, and feature representation capability of the proposed framework. Experimental results show that SFENet achieves an RMSE of 0.173 cm in the post-hoc contour reconstruction evaluation and effectively classifies multi-class features under simulated conditions. These results on the SpaceSense-Bench simulation dataset show that SFENet provides effective feature representation and accurate contour candidates under the evaluated simulation conditions. These results demonstrate the potential of the proposed framework for downstream space-debris perception and geometric reconstruction, while further validation using real sensor measurements is required to establish its robustness in practical on-orbit environments.