Scaling 3D Generative Priors to Large-Scale Scene Meshes from Multi-View Images

2026-09-06

AI Summary

本文提出了一种无需训练的框架，通过局部上下文生成和全局外观对齐的方法，解决了大规模多视角场景中几何连续性和外观一致性的问题。

Abstract

Pretrained 3D generative models produce detailed geometry and appearance but are primarily designed for object-centric generation within a limited spatial extent. Recent approaches address this limitation by partitioning large scenes into smaller spatial regions and applying pretrained 3D generative priors to each region. However, scaling tiled generation to large multi-view scenes makes it challenging to maintain local geometric continuity and global appearance consistency. We present a training-free framework for large-scale textured mesh generation from multi-view images. Our key idea is to scale tiled generation to large scenes with increased spatial detail while coordinating generation both locally and globally. We introduce local context tiled generation to improve geometric continuity between neighboring regions and global appearance alignment to reduce appearance discrepancies across distant regions. An adaptive scene decomposition further determines the number of tiles according to the input scene geometry. Experiments demonstrate improved geometric and appearance fidelity over existing approaches while enabling fine-grained generation of large-scale scenes.

Problem

• 3D generative models
• large-scale scenes
• geometric continuity
• appearance consistency
• multi-view images

Innovation

• tiled generation
• local context
• global appearance alignment
• adaptive scene decomposition