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

2026-06-23 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Computer Vision · Depth: Expert, medium

Summary

A systematic review analyzes deep learning approaches for 3D medical scene completion, compiling research contributions from 2016 to 2026. The review details the field's evolution from early voxel semantic completion paradigms, such as SSCNet, to the latest methods that integrate generative diffusion priors with real-time rendering via Gaussian splatting. It comprehensively discusses various representation paradigms, including voxel grids, point learning, implicit neural fields, transformer networks, diffusion networks, and rendering-aware 3D Gaussian primitives. The study also develops a taxonomy of contributions, highlights remaining challenges in the field, and presents a research agenda to guide the development of next-generation systems.

Key takeaway

For research scientists and computer vision engineers developing 3D medical imaging solutions, understanding the shift from voxel-based methods to generative diffusion and Gaussian splatting is crucial. You should prioritize exploring rendering-aware 3D Gaussian primitives and diffusion networks for next-generation systems, as these paradigms offer enhanced real-time capabilities and fidelity. Consider the outlined research agenda to guide your future work and address current challenges in the field.

Key insights

The field of 3D medical scene completion has rapidly evolved from voxel-based methods to generative diffusion and Gaussian splatting.

Principles

• 3D scene completion paradigms evolved from static voxel semantics to dynamic generative models.
• Representation choices significantly impact 3D scene completion capabilities.
• Diffusion priors combined with Gaussian splatting enable real-time rendering.

Topics

• 3D Scene Completion
• Deep Learning
• Medical Imaging
• Generative Models
• Gaussian Splatting
• Diffusion Networks

Code references

• hzxie/Awesome-3D-Scene-Generation
• HKU-MedAI/GEM-3D

Best for: AI Scientist, Computer Vision Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.