Quantized Visual Geometry Grounded Transformer
Authors: name: Weilun Feng; name: Haotong Qin; name: Mingqiang Wu; name: Chuanguang Yang; name: Yuqi Li; name: Xiangqi Li; name: Zhulin An; name: Libo Huang; name: Yulun Zhang; name: Michele Magno; name: Yongjun Xu
Published: 2025-09-25 15:17:11

Abstract: Learning-based 3D reconstruction models, represented by Visual Geometry
Grounded Transformers (VGGTs), have made remarkable progress with the use of
large-scale transformers. Their prohibitive computational and memory costs
severely hinder real-world deployment. Post-Training Quantization (PTQ) has
become a common practice for compressing and accelerating models. However, we
empirically observe that PTQ faces unique obstacles when compressing
billion-scale VGGTs: the data-independent special tokens induce heavy-tailed
activation distributions, while the multi-view nature of 3D data makes
calibration sample selection highly unstable. This paper proposes the first
Quantization framework for VGGTs, namely QuantVGGT. This mainly relies on two
technical contributions: First, we introduce Dual-Smoothed Fine-Grained
Quantization, which integrates pre-global Hadamard rotation and post-local
channel smoothing to mitigate heavy-tailed distributions and inter-channel
variance robustly. Second, we design Noise-Filtered Diverse Sampling, which
filters outliers via deep-layer statistics and constructs frame-aware diverse
calibration clusters to ensure stable quantization ranges. Comprehensive
experiments demonstrate that QuantVGGT achieves the state-of-the-art results
across different benchmarks and bit-width, surpassing the previous
state-of-the-art generic quantization method with a great margin. We highlight
that our 4-bit QuantVGGT can deliver a 3.7times memory reduction and
2.5times acceleration in real-hardware inference, while maintaining
reconstruction accuracy above 98\% of its full-precision counterpart. This
demonstrates the vast advantages and practicality of QuantVGGT in
resource-constrained scenarios. Our code is released in
https://github.com/wlfeng0509/QuantVGGT.