Selective structural ablation for efficient 3D point cloud signal processing

This work introduces a lightweight framework for 3D object classification in point clouds, derived from the Attention-Based Point Cloud Edge Sampling (APES) model. The approach, termed APES-Soft, is based on a systematic ablation study aimed at reducing architectural complexity while preserving classification performance. Three ablation scenarios were investigated, each altering distinct network components to determine the most effective configuration. Scenario II emerged as the most favorable trade-off variant, reaching 93.8% Accuracy alongside 93.7% Precision, 93.8% Sensitivity, 93.7% F1-Score, 93.5% Matthews Correlation Coefficient (MCC), and 89.2% Jaccard index—within the range of reported ModelNet40 results. A supplementary evaluation on ScanObjectNN PB_T50_RS further showed that Scenario II retained competitive performance on a harder benchmark, reaching 80.88%. Furthermore, Scenario II reduced training time to 20.35 hours and memory usage by 21.89%, while using 0.817M parameters, 5.885 GMACs/sample, 11.770 GFLOPs/sample, and 18.141 ms/sample for single-sample inference. Statistical analyses, including ANOVA, Tukey’s HSD, Kruskal–Wallis, and Friedman tests, were interpreted conservatively and do not support claims of formal equivalence or statistically significant superiority. Instead, the results indicate that Scenario II maintains a competitive performance profile while reducing computational cost, highlighting APES-Soft as a reliable and efficient solution for 3D object classification in resource-limited environments.