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

• URL: http://arxiv.org/abs/2609.06385v1
• Date: Sun, 06 Sep 2026 04:52:52 GMT
• Title: Scaling 3D Generative Priors to Large-Scale Scene Meshes from Multi-View Images
• Abstract summary: Pretrained 3D generative models produce detailed geometry and appearance but are primarily designed for object-centric generation within a limited spatial extent.<n>Recent approaches address this limitation by partitioning large scenes into smaller spatial regions and applying pretrained 3D generative priors to each region.<n>We present a training-free framework for large-scale textured mesh generation from multi-view images.
• Score: 5.787939851305456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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