3D Reconstruction Method Based on Nested Representation of Dynamic Gaussian Frame

Abstract

This invention discloses a nested representation 3D reconstruction method based on a dynamic Gaussian framework, belonging to the field of 3D reconstruction technology. The method includes the following steps: acquiring a dynamic 3D data sequence of a dynamic 3D scene; constructing a temporal feature sequence; defining a dynamic kernel function and updating parameters; modeling and optimizing the dynamic Gaussian process; and deriving the posterior prediction distribution of the Gaussian process based on Bayesian inference. This invention achieves full-scale feature capture based on multi-scale nested features and adaptive weighted fusion of information entropy; designs a time-dependent dynamic kernel function to construct a Gaussian process probability model to complete accurate reconstruction inference; and achieves joint iterative optimization of feature weights and kernel parameters through closed-loop feedback of prediction errors. It combines the comprehensiveness of multi-scale features, the adaptability of dynamic modeling, and the robustness of closed-loop optimization, significantly improving the reconstruction accuracy and detail reproduction of dynamic 3D data. It is applicable to various scenarios such as dynamic point clouds and dynamic meshes, and has strong engineering practical value.

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