GS-Voxel: Fitting-Free Structured Latents for Large-Scale 3DGS Generation
Summary
GS-Voxel introduces a fitting-free framework to convert 3D Gaussian Splatting reconstructions into structured latents, enabling scalable generation of large-scale aerial 3D scenes via flow models and tiled inference.
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Paper page - GS-Voxel: Fitting-Free Structured Latents for Large-Scale 3DGS Generation
Source: https://huggingface.co/papers/2608.17988
Abstract
GS-Voxel converts unstructured 3D Gaussian reconstructions into sparse structured latents to enable scalable aerial scene generation via flow models.
Many scalable latent 3D generators operate on structured tensors, whereas pre-optimized3D Gaussian Splatting(3DGS) reconstructions are unordered, spatially irregular, and vary widely in primitive count. We presentGS-Voxel, a fitting-free structured latent framework, and evaluate it for large-scale aerial 3D Gaussian scene generation.GS-Voxeldeterministically converts a compatible pre-optimized3DGSreconstruction intosparse active voxelswithout additional per-scene optimization, retaining the sub-voxel positions and rendering attributes of the selected primitives. A GS-specificfactorized VAEthen separately encodes voxel geometry and local Gaussian attributes intosparse 3D latentswhose size grows with the number of occupied voxels rather than being limited by a fixed scene-wide primitive count. We train image-conditionedflow modelsin theGS-Voxellatent space to generate aerial3DGSscenes. A key application enabled byGS-Voxelis large-area scene generation: overlap-awaretiled inferenceextends synthesis beyond a single training crop conditioned on satellite-view images. Our results show thatGS-Voxelprovides structured latents for pre-optimized aerial3DGSreconstructions, with latent capacity that grows with the number of occupied voxels.
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