| title | GS-Voxel: Fitting-Free Structured Latents for Large-Scale 3DGS Generation |
|---|---|
| authors | Ming Qian, Zijian Wang, Minchao Sun, Jincheng Xiong, Hang Zhang, Mu Xu, Chi Wang, Baoquan Chen |
| date | 2026-08-18 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2608.17988 |
| affiliation | Alibaba AMAP CV Lab |
| upvotes | 5 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | Many scalable latent 3D generators operate on structured tensors, whereas pre-optimized 3D Gaussian Splatting (3DGS) reconstructions are unordered, spatially irregular, and vary widely in primitive count. We present GS-Voxel, a fitting-free structured latent framework, and evaluate it for large-scale aerial 3D Gaussian scene generation. GS-Voxel deterministically converts a compatible pre-optimized 3DGS reconstruction into sparse active voxels without additional per-scene optimization, retaining the sub-voxel positions and rendering attributes of the selected primitives. A GS-specific factorized VAE then separately encodes voxel geometry and local Gaussian attributes into sparse 3D latents whose size grows with the number of occupied voxels rather than being limited by a fixed scene-wide primitive count. We train image-conditioned flow models in the GS-Voxel latent space to generate aerial 3DGS scenes. A key application enabled by GS-Voxel is large-area scene generation: overlap-aware tiled inference extends synthesis beyond a single training crop conditioned on satellite-view images. Our results show that GS-Voxel provides structured latents for pre-optimized aerial 3DGS reconstructions, with latent capacity that grows with the number of occupied voxels. |
cite
@misc{qian2026gs,
title={GS-Voxel: Fitting-Free Structured Latents for Large-Scale 3DGS Generation},
author={Ming Qian and Zijian Wang and Minchao Sun and Jincheng Xiong and Hang Zhang and Mu Xu and Chi Wang and Baoquan Chen},
year={2026},
eprint={2608.17988},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2608.17988}
}