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

This study presents a systematic review of advancements in 3D scene completion over the past decade, highlighting the transition from voxel semantic completion models like SSCNet to contemporary approaches integrating generative diffusion priors and real-time rendering via Gaussian splatting techniques. It covers various representation paradigms, including voxel grids, point learning, implicit neural fields, transformer networks, and diffusion networks, while also proposing a taxonomy for better understanding of the field's evolution and outlining future research directions. This comprehensive analysis is crucial for practitioners looking to adopt or improve upon existing methodologies in 3D scene understanding and related applications in robotics and augmented reality.