系统梳理十年3D医学场景补全进展，涵盖从体素到生成式渲染的范式演进
Deep Learning Approaches for 3D Medical Scene Completion: From Geometric Modeling to Generative Paradigms
- 按表示范式分类，涵盖体素、点云、隐式神经场等方法
- 提出基于渲染感知3D高斯原语的最新生成式框架，支持实时重建
- 适合关注医学图像补全与生成式3D建模的研究者阅读
三维场景补全已成为计算机视觉与机器人领域的关键问题，广泛应用于自动驾驶和增强现实。本研究系统回顾了2016至2026年间该领域的重要进展，推动了从以SSCNet为代表的体素语义补全范式，向结合生成式扩散先验与实时渲染的高斯点阵技术的最新范式演进。文章讨论了多种表示范式的发展，包括体素网格、点学习、隐式神经场、Transformer网络、扩散网络以及基于渲染感知3D高斯原语的最新方法。通过全面分析近十年贡献，构建了领域分类体系，并指出现有挑战与未来研究方向，为下一代系统的开发提供明确路径。
原文摘要 · Abstract (English)
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 made in the last ten years, i.e., 2016 to 2026, which has revolutionized the field from the voxel semantic completion paradigm represented by SSCNet to the latest paradigm that combines generative diffusion priors with real-time rendering using a Gaussian splatting technique. The evolution in representation paradigms, such as voxel grids, point learning, implicit neural fields, transformer networks, diffusion networks, and the latest paradigm based on rendering-aware 3D Gaussian primitives, has been discussed in this study. A comprehensive analysis has been carried out on the contributions made in the last ten years, and a taxonomy has been developed to provide a clear idea about the contributions made in the field. The study has also discussed the research contributions made in the field, along with the challenges that still need to be addressed. Finally, the study has presented a research agenda that will provide a clear idea about the directions that can be followed in the development of the next-generation system
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