Rethinking Image-to-3D Generation with Sparse Queries: Efficiency, Capacity, and Input-View Bias
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866918448619061248 |
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| author | Xu, Zhiyuan Liu, Jiuming Chen, Yuxin Tomizuka, Masayoshi Xu, Chenfeng Peng, Chensheng |
| author_facet | Xu, Zhiyuan Liu, Jiuming Chen, Yuxin Tomizuka, Masayoshi Xu, Chenfeng Peng, Chensheng |
| contents | We present SparseGen, a novel framework for efficient image-to-3D generation, which exhibits low input-view bias while being significantly faster. Unlike traditional approaches that rely on dense volumetric grids, triplanes, or pixel-aligned primitives, we model scenes with a compact sparse set of learned 3D anchor queries and a learned expansion operator that decodes each transformed query into a small local set of 3D Gaussian primitives. Trained under a rectified-flow reconstruction objective without 3D supervision, our model learns to allocate representation capacity where geometry and appearance matter, achieving significant reductions in memory and inference time while preserving multi-view fidelity. We introduce quantitative measures of input-view bias and utilization to show that sparse queries reduce overfitting to conditioning views while being representationally efficient. Our results argue that sparse set-latent expansion is a principled, practical alternative for efficient 3D generative modeling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_13905 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Rethinking Image-to-3D Generation with Sparse Queries: Efficiency, Capacity, and Input-View Bias Xu, Zhiyuan Liu, Jiuming Chen, Yuxin Tomizuka, Masayoshi Xu, Chenfeng Peng, Chensheng Computer Vision and Pattern Recognition We present SparseGen, a novel framework for efficient image-to-3D generation, which exhibits low input-view bias while being significantly faster. Unlike traditional approaches that rely on dense volumetric grids, triplanes, or pixel-aligned primitives, we model scenes with a compact sparse set of learned 3D anchor queries and a learned expansion operator that decodes each transformed query into a small local set of 3D Gaussian primitives. Trained under a rectified-flow reconstruction objective without 3D supervision, our model learns to allocate representation capacity where geometry and appearance matter, achieving significant reductions in memory and inference time while preserving multi-view fidelity. We introduce quantitative measures of input-view bias and utilization to show that sparse queries reduce overfitting to conditioning views while being representationally efficient. Our results argue that sparse set-latent expansion is a principled, practical alternative for efficient 3D generative modeling. |
| title | Rethinking Image-to-3D Generation with Sparse Queries: Efficiency, Capacity, and Input-View Bias |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.13905 |