Simultaneous Monitoring of Shape and Surface Color via 4D Point Clouds: A Registration-free Approach
Abstract
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.
Related
- Continuous and Differentiable Neural Network Representations of the Surfzone Sea-Surface from Lidar Point Clouds
- Shape Differentiation and Anisotropy Onset of Quadratic Invariant-Manifold Jets in Surface Navier–Stokes Flow
- Hyperelastic Modeling of Cardiac Tissue Deformation for Data Registration
- Deterministic and stochastic particle methods for the Fokker-Planck equation in S--formulation with application to point set registration
- ConSEAL: Connectivity-Informed Streamline Endpoint Alignment for Diffeomorphic Cortical Registration
BibTeX
@misc{patalano2026simultaneous,
title = {{Simultaneous Monitoring of Shape and Surface Color via 4D Point Clouds: A Registration-free Approach}},
author = {Mariafrancesca Patalano and Giovanna Capizzi and Kamran Paynabar},
howpublished = {arXiv},
year = {2026},
month = aug,
doi = {10.48550/arxiv.2605.08753},
eprint = {2605.08753},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2605.08753},
}
RIS
TY - UNPB
TI - Simultaneous Monitoring of Shape and Surface Color via 4D Point Clouds: A Registration-free Approach
AU - Mariafrancesca Patalano
AU - Giovanna Capizzi
AU - Kamran Paynabar
PY - 2026
DA - 2026/08/30
PB - arXiv
DO - 10.48550/arxiv.2605.08753
UR - https://arxiv.org/abs/2605.08753
AB - 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.
ER -