An Energy-Driven Adaptive Reconstruction Method for Non-Cooperative Satellite Point Clouds

To address the challenges of sparse point clouds, incomplete structures, and noise interference in on-orbit perception of non-cooperative satellites, this study develops a reconstruction method that integrates multi-frame point cloud fusion with an energy-driven optimization strategy. A self-developed experimental platform for non-cooperative targets is employed to acquire multi-view point cloud data, which are subsequently fused to improve geometric coverage and structural completeness. Based on this dataset, four representative 3D reconstruction algorithms are systematically evaluated. The results indicate that the Local Delaunay method achieves a favorable balance between geometric accuracy and structural preservation (RMSE = 6.152 mm). However, its performance is limited by reliance on multiple geometric thresholds and fixed-scale constraints, leading to reduced robustness and stability under challenging conditions. To overcome these limitations, an energy-based normal-consistent adaptive reconstruction method (ENALD) is developed within the Local Delaunay framework. The proposed approach incorporates a normal consistency constraint and an adaptive scale mechanism and constructs a multi-feature energy model to guide triangle selection. An energy-ranking strategy is further introduced, retaining the top 50% of candidates to improve reconstruction quality. Experimental results demonstrate that, compared with the conventional Local Delaunay method, ENALD reduces the maximum deviation, average deviation, and RMSE by 21.3%, 10.0%, and 11.9%, respectively, while improving computational efficiency by approximately 45.4%. In addition, the reconstructed surfaces exhibit enhanced structural continuity and better preservation of local details, without introducing noticeable topological artifacts.