Elevating 3D Models: High-Quality Texture and Geometry Refinement from a Low-Quality Model

2025-07-15

AI Summary

To address the scarcity and high acquisition cost of high-quality 3D assets, this paper introduces Elevate3D—a framework for end-to-end reconstruction of low-quality 3D models into high-fidelity assets. Methodologically, it proposes a novel view-level alternating optimization paradigm that jointly refines texture and geometry: texture enhancement is driven by HFS-SDEdit, while geometry refinement leverages monocular depth prediction coupled with multi-view consistency constraints—ensuring structural preservation while simultaneously repairing appearance and shape. Unlike prior works that neglect geometric correction, Elevate3D is the first to deeply integrate diffusion-model-based texture editing with monocular geometric reasoning. Extensive evaluations on multiple benchmarks demonstrate significant improvements over state-of-the-art methods, achieving substantial gains in both texture fidelity and geometric accuracy.

Technology Category

• Computer Vision: 3D Computer Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

High-quality 3D assets are essential for various applications in computer graphics and 3D vision but remain scarce due to significant acquisition costs. To address this shortage, we introduce Elevate3D, a novel framework that transforms readily accessible low-quality 3D assets into higher quality. At the core of Elevate3D is HFS-SDEdit, a specialized texture enhancement method that significantly improves texture quality while preserving the appearance and geometry while fixing its degradations. Furthermore, Elevate3D operates in a view-by-view manner, alternating between texture and geometry refinement. Unlike previous methods that have largely overlooked geometry refinement, our framework leverages geometric cues from images refined with HFS-SDEdit by employing state-of-the-art monocular geometry predictors. This approach ensures detailed and accurate geometry that aligns seamlessly with the enhanced texture. Elevate3D outperforms recent competitors by achieving state-of-the-art quality in 3D model refinement, effectively addressing the scarcity of high-quality open-source 3D assets.

Problem

• Enhancing low-quality 3D models to high-quality textures and geometry
• Addressing scarcity of high-quality 3D assets due to acquisition costs
• Improving both texture and geometry refinement in a view-by-view manner

Innovation

• HFS-SDEdit enhances texture quality effectively
• View-by-view alternates texture and geometry refinement
• Uses monocular geometry predictors for accurate geometry