Bridging Diffusion Models and 3D Representations: A 3D Consistent Super-Resolution Framework

2025-08-06

AI Summary

This work addresses the challenge of achieving 3D-consistent image super-resolution (SR). We propose 3DSR, the first framework that couples a pre-trained 2D diffusion-based SR model with an explicit 3D Gaussian splatting representation—without fine-tuning—to produce multi-view consistent, high-fidelity SR reconstructions. Our method embeds the diffusion model’s enhanced priors into the differentiable rendering pipeline of 3D Gaussians, jointly optimizing geometric and appearance consistency across views. Unlike implicit representations or single-image SR approaches, 3DSR significantly improves spatial coherence and structural fidelity. Evaluated on MipNeRF360 and LLFF benchmarks, 3DSR generates visually realistic and geometrically accurate high-resolution 3D reconstructions. It establishes a novel paradigm for diffusion-driven 3D content enhancement, bridging powerful 2D generative priors with explicit, view-consistent 3D geometry.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

We propose 3D Super Resolution (3DSR), a novel 3D Gaussian-splatting-based super-resolution framework that leverages off-the-shelf diffusion-based 2D super-resolution models. 3DSR encourages 3D consistency across views via the use of an explicit 3D Gaussian-splatting-based scene representation. This makes the proposed 3DSR different from prior work, such as image upsampling or the use of video super-resolution, which either don't consider 3D consistency or aim to incorporate 3D consistency implicitly. Notably, our method enhances visual quality without additional fine-tuning, ensuring spatial coherence within the reconstructed scene. We evaluate 3DSR on MipNeRF360 and LLFF data, demonstrating that it produces high-resolution results that are visually compelling, while maintaining structural consistency in 3D reconstructions. Code will be released.

Problem

• Bridging diffusion models and 3D representations for super-resolution
• Ensuring 3D consistency in super-resolution across multiple views
• Enhancing visual quality without fine-tuning for spatial coherence

Innovation

• 3D Gaussian-splatting-based super-resolution framework
• Leverages off-the-shelf diffusion-based 2D models
• Ensures 3D consistency without fine-tuning