Publication

Abstract : 3D point clouds are data points defining the morphology of environments, and completion refers to the reconstruction of missing points. 6G Internet of Things (6G-IoT) connected with 3D mapping devices needs reliable, consistent, high-fidelity real-time point cloud completion for accurate environment registration. Trustworthy AI, modeled with dependable Deep Learning (DL), enables reliable and robust point completion with spatial-geometrical consistency for deployment with 6G-IoT devices. Although several DL-based completion techniques are integrated with 6G-IoT devices, they have reliability issues, limiting key trustworthy AI characteristics. This research focuses on the reliability and robustness aspects of trustworthy AI to propose Point-KAN, a dependable real-time 3D point cloud completion model for 6G IoT-connected 3D mapping devices. Point-KAN integrates multi-head attention and Kolmogorov-Arnold Networks (KAN) within the modules of Attention Enhanced-Embedded Feature Collector (AEFC) and KAN-Enhanced Feature Mapper (KEFM) for trustworthy point cloud completion. Empirical evaluations on the ShapeNet demonstrate the superiority of Point-KAN against state-of-the-art (SOTA). Results concrete Point-KAN’s evolution as a trustworthy AI framework ensures reliability and robustness for real-time deployment in 6G-IoT-connected devices, facilitating 3D environment mapping.