本文全面回顾了过去十年（2016年至2026年）开发的三维医学场景补全技术。它追溯了从早期的基于体素的方法到目前将生成扩散模型与使用高斯泼溅的实时渲染相结合的方法的演变。该综述按不同表示范式对进展进行了分类，包括点学习、隐式神经场和Transformer网络，并确定了持续的挑战和未来的研究方向。 AI
影响 提供了三维场景补全进展的结构化概述，可能指导人工智能驱动的医学成像和机器人领域的未来研究。
排序理由 该条目是发表在arXiv上的系统性综述论文。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv:2606.24180v1 Announce Type: cross Abstract: Three-dimensional scene completion has evolved as a major problem in computer vision and robotics, and its applications are diverse, including autonomous navigation and augmented reality. In this study, a systematic review has bee…
Three-dimensional scene completion has evolved as a major problem in computer vision and robotics, and its applications are diverse, including autonomous navigation and augmented reality. In this study, a systematic review has been conducted to compile the research contributions …