Simultaneous Monitoring of Shape and Surface Color via 4D Point Clouds: A Registration-free Approach
For quality monitoring in advanced manufacturing, this work provides a registration-free method to detect both shape and color anomalies in 4D point clouds, eliminating error-prone preprocessing.
The paper proposes a registration-free framework (SMAC) for simultaneous monitoring of shape and surface color via 4D point clouds, using Laplace-Beltrami operator spectral properties. Monte Carlo simulations and a case study on functionally graded materials show effective detection of subtle defects with diagnostic capabilities.
Advanced manufacturing technologies allow for the production of intricate parts featuring high shape complexity and spatially-varying material composition. Data fusion of point clouds with chromatic attributes provides 4D point clouds, a compact and informative representation that encodes both shape and material information. In this paper, we present a registration-free framework for Simultaneous Monitoring of shApe and Color (SMAC) via 4D point clouds. The proposed framework leverages Laplace-Beltrami operator spectral properties to capture and monitor geometric features and the relationship between shape and surface color. A combined monitoring scheme is proposed to effectively detect shape deformations and color anomalies, along with a spatially-aware post-signal diagnostic procedure to determine the source of change and localize color anomalies. Importantly, neither component relies on registration or mesh reconstruction, eliminating error-prone and computationally expensive preprocessing steps. A Monte Carlo simulation study and a case study on functionally graded materials demonstrate that SMAC achieves effective detection performance, particularly for subtle defects, while providing diagnostic capabilities to identify the source and location of anomalies.