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

Evolution of 3D Scene Completion

3D scene completion addresses the challenge of inferring missing geometric and semantic information from partial sensor data, such as occluded LiDAR scans or noisy RGB-D images. Over the past decade (2016–2026), the field has undergone a significant paradigm shift. Early approaches relied on volumetric convolutional neural networks (CNNs) using voxel grids, which provided structured data but suffered from cubic memory scaling. As the field matured, researchers transitioned toward point-based methods for better efficiency, followed by implicit neural representations that allow for continuous, high-resolution surface modeling.

Current Paradigms and Innovations

The current state-of-the-art is defined by the integration of generative diffusion models and 3D Gaussian splatting. Diffusion models provide powerful priors for synthesizing plausible scene geometry even in highly sparse scenarios, while Gaussian splatting enables real-time rendering of complex 3D scenes. Modern architectures are increasingly hybrid, combining the global context awareness of transformers with the geometric precision of implicit fields and the rendering speed of Gaussian primitives. This shift marks a move from simple geometric reconstruction toward comprehensive scene understanding suitable for embodied AI and digital twins.

Challenges and Future Directions

Despite these advancements, several critical challenges remain. High-resolution completion remains computationally expensive, and maintaining temporal consistency in dynamic scenes is difficult. Furthermore, there is a growing need for foundation models that can generalize across diverse environments without requiring task-specific retraining. The research agenda for the next generation of systems emphasizes the development of "Green AI"—models that achieve high fidelity with lower energy consumption—and the integration of uncertainty estimation to improve reliability in safety-critical applications like autonomous navigation.