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arXiv · 2606.24180

Deep Learning Approaches for 3D Medical Scene Completion: From Geometric Modeling to Generative Paradigms

Afifa Khaled·Said Jadid Abdulkadir·Majdy Mohamed Eltayeb Eltahir
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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 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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Afifa Khaled, Said Jadid Abdulkadir, Majdy Mohamed Eltayeb Eltahir. 2026-06-23. Deep Learning Approaches for 3D Medical Scene Completion: From Geometric Modeling to Generative Paradigms. https://arxiv.org/abs/2606.24180

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