VersaQ-3D: A Reconfigurable Accelerator Enabling Feed-Forward and Generalizable 3D Reconstruction via Versatile Quantization
Yipu Zhang, Jintao Cheng, Xingyu Liu, Zeyu Li, Carol Jingyi Li, Jin Wu, Lin Jiang, Yuan Xie, Jiang Xu, Wei Zhang

TL;DR
VersaQ-3D introduces a reconfigurable accelerator and a novel quantization method for VGGT, enabling efficient, accurate, and low-power 3D reconstruction on edge devices without scene-specific calibration.
Contribution
It presents the first calibration-free, scene-agnostic 4-bit quantization for VGGT and a reconfigurable hardware accelerator supporting multiple precisions, improving efficiency and deployment feasibility.
Findings
Preserves 98-99% accuracy at W4A8 quantization.
Outperforms prior methods by 1.61x-2.39x at W4A4.
Achieves 5.2x-10.8x speedup over edge GPUs.
Abstract
The Visual Geometry Grounded Transformer (VGGT) enables strong feed-forward 3D reconstruction without per-scene optimization. However, its billion-parameter scale creates high memory and compute demands, hindering on-device deployment. Existing LLM quantization methods fail on VGGT due to saturated activation channels and diverse 3D semantics, which cause unreliable calibration. Furthermore, VGGT presents hardware challenges regarding precision-sensitive nonlinear operators and memory-intensive global attention. To address this, we propose VersaQ-3D, an algorithm-architecture co-design framework. Algorithmically, we introduce the first calibration-free, scene-agnostic quantization for VGGT down to 4-bit, leveraging orthogonal transforms to decorrelate features and suppress outliers. Architecturally, we design a reconfigurable accelerator supporting BF16, INT8, and INT4. A unified…
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Taxonomy
TopicsAdvanced Vision and Imaging · Advanced Optical Imaging Technologies · Computer Graphics and Visualization Techniques
