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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Mixtures of convolutional measures on low-dimensional affine spaces admit unique identifiability in semi-parametric settings and posterior contraction rates under convex polytope support assumptions in a well-specified Bayesian regime.
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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Learning Mixtures of Nonparametric and Convolutional Measures on Effectively Low-dimensional Affine Spaces
Mixtures of convolutional measures on low-dimensional affine spaces admit unique identifiability in semi-parametric settings and posterior contraction rates under convex polytope support assumptions in a well-specified Bayesian regime.