Quantized Visual Geometry Grounded Transformer
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.
68d63d1d8ccd91bdd39ffeb9
Weilun Feng
admin_assigned
2025-09-26T16:20:57.222Z
68d63e44ccf464a96ac18bcb
/avatars/01b93c353a7231fa9e053ba9edcb2b06.svg
Weilun Feng
user
wlfeng
68d63d1d8ccd91bdd39ffeba
Haotong Qin
admin_assigned
2025-09-26T16:21:03.249Z
65c49589c0b1921e19260a8d
/avatars/7ce9af8c627f2a0c3db6bde82290ee1f.svg
Haotong Qin
user
HaotongQin
68d63d1d8ccd91bdd39ffebb
Mingqiang Wu
admin_assigned
2025-09-26T16:21:09.164Z
684035f695d9353972945401
/avatars/5806e87932089e4bb5163435ec4aa0e8.svg
12312
user
mingqiangwu
68d63d1d8ccd91bdd39ffebc
Chuanguang Yang
68d63d1d8ccd91bdd39ffebd
Yuqi Li
68d63d1d8ccd91bdd39ffebe
Xiangqi Li
68d63d1d8ccd91bdd39ffebf
Zhulin An
admin_assigned
2025-09-26T16:21:23.315Z
67e6123c72a010b0a98b9ee0
/avatars/71c391abedd04047a7aadf08a8533c7a.svg
Zhulin An
user
anzhulin
68d63d1d8ccd91bdd39ffec0
Libo Huang
68d63d1d8ccd91bdd39ffec1
Yulun Zhang
admin_assigned
2025-09-26T16:21:35.408Z
647a0400bfaa9e96b8529a7f
/avatars/41161df5af1e6818d9e021389db316dd.svg
Yulun Zhang
user
yulunzhang
68d63d1d8ccd91bdd39ffec2
Michele Magno
68d63d1d8ccd91bdd39ffec3
Yongjun Xu
Quantized Visual Geometry Grounded Transformer
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.
QuantVGGT, a quantization framework for Visual Geometry Grounded Transformers, achieves state-of-the-art results with memory reduction and acceleration while maintaining high reconstruction accuracy.
Visual Geometry Grounded Transformers VGGTs Post-Training Quantization PTQ Dual-Smoothed Fine-Grained Quantization Hadamard rotation channel smoothing Noise-Filtered Diverse Sampling frame-aware diverse calibration clusters real-hardware inference