A Flexible Hierarchical Framework for Implicit 3D Characterization of Bionic Devices

In practical applications, integrating three-dimensional models of bionic devices with simulation systems can predict their behavior and performance under various operating conditions, providing a basis for subsequent engineering optimization and improvements. This study proposes a framework for characterizing three-dimensional models of objects, focusing on extracting 3D structures and generating high-quality 3D models. The core concept involves obtaining the density output of the model from multiple images to enable adaptive boundary surface detection. The framework employs a hierarchical octree structure to partition the 3D space based on surface and geometric complexity. This approach includes recursive encoding and decoding of the octree structure and surface geometry, ultimately leading to the reconstruction of the 3D model. The framework has been validated through a series of experiments, yielding positive results.