VIAFormer: Voxel-Image Alignment Transformer for High-Fidelity Voxel Refinement
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914268385902592 |
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| author | Fang, Tiancheng Pan, Bowen Chen, Lingxi Lyu, Jiangjing Lyu, Chengfei Niu, Chaoyue Wu, Fan |
| author_facet | Fang, Tiancheng Pan, Bowen Chen, Lingxi Lyu, Jiangjing Lyu, Chengfei Niu, Chaoyue Wu, Fan |
| contents | We propose VIAFormer, a Voxel-Image Alignment Transformer model designed for Multi-view Conditioned Voxel Refinement--the task of repairing incomplete noisy voxels using calibrated multi-view images as guidance. Its effectiveness stems from a synergistic design: an Image Index that provides explicit 3D spatial grounding for 2D image tokens, a Correctional Flow objective that learns a direct voxel-refinement trajectory, and a Hybrid Stream Transformer that enables robust cross-modal fusion. Experiments show that VIAFormer establishes a new state of the art in correcting both severe synthetic corruptions and realistic artifacts on the voxel shape obtained from powerful Vision Foundation Models. Beyond benchmarking, we demonstrate VIAFormer as a practical and reliable bridge in real-world 3D creation pipelines, paving the way for voxel-based methods to thrive in large-model, big-data wave. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_13664 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | VIAFormer: Voxel-Image Alignment Transformer for High-Fidelity Voxel Refinement Fang, Tiancheng Pan, Bowen Chen, Lingxi Lyu, Jiangjing Lyu, Chengfei Niu, Chaoyue Wu, Fan Computer Vision and Pattern Recognition I.2.10; I.4.5 We propose VIAFormer, a Voxel-Image Alignment Transformer model designed for Multi-view Conditioned Voxel Refinement--the task of repairing incomplete noisy voxels using calibrated multi-view images as guidance. Its effectiveness stems from a synergistic design: an Image Index that provides explicit 3D spatial grounding for 2D image tokens, a Correctional Flow objective that learns a direct voxel-refinement trajectory, and a Hybrid Stream Transformer that enables robust cross-modal fusion. Experiments show that VIAFormer establishes a new state of the art in correcting both severe synthetic corruptions and realistic artifacts on the voxel shape obtained from powerful Vision Foundation Models. Beyond benchmarking, we demonstrate VIAFormer as a practical and reliable bridge in real-world 3D creation pipelines, paving the way for voxel-based methods to thrive in large-model, big-data wave. |
| title | VIAFormer: Voxel-Image Alignment Transformer for High-Fidelity Voxel Refinement |
| topic | Computer Vision and Pattern Recognition I.2.10; I.4.5 |
| url | https://arxiv.org/abs/2601.13664 |