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

2026-06-23 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Computer Vision · Depth: Expert, quick

Summary

A systematic review analyzes deep learning approaches for 3D medical scene completion, covering research contributions from 2016 to 2026. This comprehensive study details the field's evolution from early voxel semantic completion paradigms, such as SSCNet, to the latest methods combining generative diffusion priors with real-time rendering using Gaussian splatting techniques. The review discusses diverse representation paradigms, including voxel grids, point learning, implicit neural fields, transformer networks, diffusion networks, and rendering-aware 3D Gaussian primitives. It provides a taxonomy, analyzes challenges, and presents a research agenda for developing next-generation systems applicable in computer vision, robotics, autonomous navigation, and augmented reality.

Key takeaway

For computer vision engineers developing 3D medical imaging solutions, understanding the rapid evolution from voxel-based methods to generative diffusion and Gaussian splatting is crucial. Your project's performance and efficiency will benefit from adopting these newer rendering-aware 3D Gaussian primitives. Reviewing the proposed research agenda can guide your future development efforts and help address current challenges in autonomous navigation and augmented reality.

Key insights

The field of 3D medical scene completion has rapidly evolved from voxel-based methods to advanced generative and rendering-aware paradigms.

Method

The study conducted a systematic review, compiling research from 2016-2026, analyzing contributions, developing a taxonomy, and discussing challenges and a research agenda for future systems.

In practice

• Autonomous navigation systems
• Augmented reality applications
• Next-generation 3D systems

Topics

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

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.