SMAC detects shape deformations and color anomalies in 4D point clouds using Laplace-Beltrami spectral properties without registration or mesh reconstruction.
Technometrics , volume =
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A Bayesian optimal experimental design framework with Gaussian approximation of expected information gain and surrogate Fisher information enables optimized uniaxial tests that significantly improve identifiability of history-dependent constitutive parameters over random designs.
citing papers explorer
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Simultaneous Monitoring of Shape and Surface Color via 4D Point Clouds: A Registration-free Approach
SMAC detects shape deformations and color anomalies in 4D point clouds using Laplace-Beltrami spectral properties without registration or mesh reconstruction.
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Optimal Experimental Design for Reliable Learning of History-Dependent Constitutive Laws
A Bayesian optimal experimental design framework with Gaussian approximation of expected information gain and surrogate Fisher information enables optimized uniaxial tests that significantly improve identifiability of history-dependent constitutive parameters over random designs.