Yipu Zhang

Yipu Zhang, Jintao Cheng, Xingyu Liu et al.

3D reconstruction and view synthesis are fundamental to AR/VR, robotics, and digital twins. The Visual Geometry Grounded Transformer (VGGT) enables strong feed-forward 3D reconstruction while its billion-parameter scale limits on-device deployment. LLM-oriented quantization methods fail on VGGT due to saturated activation channels that resist low-bit quantization and diverse 3D semantics that impede calibration. VGGT further poses hardware challenges from multi-precision architecture support and long-sequence global attention with excessive memory demands. We propose VersaQ-3D, an algorithm-architecture co-design framework for efficient VGGT inference. At the algorithm level, we present the first calibration-free, input-agnostic quantization method for VGGT, leveraging transform coding to suppress outliers and preserve structural weight features, enabling robust low-bit inference down to 4 bits. At the architecture level, we design a reconfigurable accelerator with a hierarchical multi-precision compute unit (BF16/INT8/INT4) that executes both linear and non-linear operators within a shared systolic datapath, reducing end-to-end latency by 77%. A two-stage recomputation-based tiling strategy further cuts runtime by 7% by alleviating on-chip memory pressure for long-sequence attention. Evaluations across various datasets show that VersaQ-3D incurs negligible accuracy loss at W4A8 and consistently achieves leading accuracy at W4A4 over prior quantization methods across diverse scenes. The co-designed accelerator delivers 5.4$\times$-22.0$\times$ speedup over edge GPUs and 2.2$\times$-3.0$\times$ over prior quantization-based accelerators under iso-PE-area comparison, enabling instant and energy-efficient feed-forward 3D reconstruction on edge devices.

Yipu Zhang, Jintao Cheng, Weilun Feng et al.

Feed-forward 3D reconstruction models, represented by Visual Geometry Grounded Transformer (VGGT), jointly predict multiple visual geometry tasks such as depth estimation, camera pose prediction, and point cloud reconstruction in a single forward pass. They have been widely adopted in 3D vision applications, but their billion-scale parameters bring substantial memory and computation overhead, posing challenges for on-device deployment. Post-Training Quantization (PTQ) is an effective technique to reduce this overhead. Existing PTQ methods for feed-forward 3D models mainly focus on handling heavy-tailed activation distributions and constructing diverse calibration datasets. However, we observe that feed-forward 3D models predict multiple geometric attributes through a shared backbone, where different transformer blocks and hidden channels contribute distinctly to each task, resulting in substantially different sensitivities to quantization errors across tasks, blocks, and channels. Consequently, treating all tasks equally over-emphasizes insensitive tasks and causes significant accuracy loss on the sensitive ones. To address this issue, we propose Fisher-Guided Quantization (FGQ) for feed-forward 3D reconstruction models. Specifically, FGQ uses the diagonal Fisher information matrix to quantify the different sensitivities across tasks, blocks, and channels, and incorporates these sensitivities into the Learnable Affine Transformation during calibration to better preserve the channels and blocks most critical to each task. Extensive experiments across camera pose estimation, point map reconstruction, and depth estimation show that FGQ consistently outperforms state-of-the-art quantization baselines on VGGT, achieving up to 39% relative improvement under the 4-bit quantization.