Neural rendering-based fast scene geometry modeling and retrieval method for digital twin assets
Virtual-real fusion represents a quintessential facet of digital twin technology.In digital twins,virtual scenes are predominantly realized through geometric modeling techniques.To address the challenge of limited auto-mation and excessive dependence on manual intervention in scene geometric modeling,resulting in high costs and in-efficiencies,a digital twin geometric scene modeling approach was proposed.In this approach,the neural rendering technology was introduced to gather point cloud data from physical entities.Subsequently,a deep learning-based method was devised for the semantic mapping of point cloud models to 3D CAD models,which was applied to re-trieving 3D CAD models from the digital twin geometric model asset library.A training dataset was curated,and a digital twin geometric model asset library was constructed for experimental validation.The efficacy of the proposed approach was corroborated through comparative experiments and case studies involving the disassembly of decom-missioned batteries.The results affirmed that the proposed method exhibited significantly lower costs and markedly heightened efficiency than alternative digital twin scene geometric modeling methodologies.
digital twinscene geometric modelingneural renderingmodel retrieval
万方数据
维普
