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arXiv · 2601.13664

VIAFormer: Voxel-Image Alignment Transformer for High-Fidelity Voxel Refinement

Tiancheng Fang·Bowen Pan·Lingxi Chen·Jiangjing Lyu·Chengfei Lyu·Chaoyue Niu·Fan Wu
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Abstract

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.

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Tiancheng Fang, Bowen Pan, Lingxi Chen, Jiangjing Lyu, Chengfei Lyu, Chaoyue Niu, Fan Wu. 2026-01-20. VIAFormer: Voxel-Image Alignment Transformer for High-Fidelity Voxel Refinement. https://arxiv.org/abs/2601.13664

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