Quantized Visual Geometry Grounded Transformer
Authors: Weilun Feng
Haotong Qin
Mingqiang Wu
Chuanguang Yang
Yuqi Li
Xiangqi Li
Zhulin An
Libo Huang
Yulun Zhang
Michele Magno
Yongjun Xu
Venue: ICLR 2026 Poster (2026)
Keywords: Geometry Grounded
Model Quantization
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Learning-based 3D reconstruction models, represented by Visual Geometry Grounded Transformers (VGGTs), have achieved remarkable progress with large-scale transformers. Their prohibitive computational and memory costs severely hinder real-world deployment. Post-Training Quantization (PTQ) has emerged as a common practice to compress and accelerate 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 **Quant**ization framework for **VGGT**s, 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 robustly mitigate heavy-tailed distributions and inter-channel variance. 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 **3.7$\times$** memory reduction and **2.5$\times$** acceleration in real-hardware inference, while preserving over **98\%** reconstruction accuracy of the full-precision counterparts. This demonstrates the vast advantages and practicality of QuantVGGT in resource-constrained scenarios.

Published as a conference paper at ICLR 2026
QUANTIZED VISUAL GEOMETRY GROUNDED TRANSFORMER
Weilun Feng Haotong Qin Mingqiang :u
Chuanguang Yang", YuqiLi",
Xiangqi Li Zhulin An Libo Huang', Yulun Zhang', Michele Magno², Yongjun Xu "ttate Key Laboratory of Al Safety, Institute of Computing Technology, Chinese Academy of Sciences PUniversity of Chinese Academy of Sciences 'ETH Zürich
*City College of New York, City University of New York, USA Shanghai Jiao Tong University {fengwei i Tun24s, yangchuanquang, [haoteng. . qin, michele Jaaaggo@@ppp..i ee [yugil1010602 www. hwwnglibo, wtw.:/eeeeeeee.e.mooo ethz. wumingqi anga5mmaggaaaammm 2em22mmmmmlls ucas. ac. ca,
anzhul i syj]eict.es
VGGT (FP16) Speed Opt. 1.0x
QuantVGGT (W4A4) Speed Opt. 2.5x
Memory Opt. 1.0x
Memory Opt. 3.7x
Figure 1: QuantVGGT effectively quantizes VGGT (Wang et al., 2025a) to W4A4 without com- promising visual quality while bringing x.5x speedup and 3.7x compression.
ABSTRACT
Learning-based 3D reconstruction models, represented by Visual Geometry Grounded Transformers (VGGTs), have made remarkable progress with the use large-scale transformers. Their prohibitive computational and memory costs severely hinder real-world deployment. Post-Traine fost-Training Quantization (PTQ) has become a common practice for compressing and accelerating models. How- ever, we empirically observe that PTQ faces unique obstacles when compressing billion-scale VGGTs: the data-independent special tokens induce heavy-tailed ac- tivation 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 tech- nical contributions: First. we introduce Dual- Oual-Smoothed Shne-Grained Quanti- zation, which integrates pre-global Hadamard rotation and post-local channel smoothing to mitigate heavy-tailed distributions and inter-channel variance ro- bustly. Second, we design Noise-Filteree Diverse Sampling, which filters out- liers via deep-layer statistics and constructs frame-aware diverse calibration clus- ters to ensure stable quantization ranges. Comprehensive experiments demon- strate that QuantVGGT achieves the state-of-the-art results across different bench- marks and bit-width, surpassing the previous state-of-the-an generic quantization method with great margin. We highlight that 4-bit QuantVGGT can de- liver a 3.7x x memory reduction and 2.5x acceleration in real-hardware inference,
Equual contribution.
Corresponding authors: Zhulin An, anzhulin @ ict.ac.en; Chuanguang Yang, yangchuangang@@cct.acccm

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Published as a conference paper at ICLR 2026
while maintaining reconstruction accuracy above 98% of its full-precision coun- terpart. This demonstrates the vast advantages and practicality of QuantVGGT in resource-consstrinned scenarios. Our code is released in https: githb.. com/ w1 feng0009/ /
INTRODUCTION
Recent advances in learning-based 3D reconstruction have demonstrated unprecedented capabilities in recovering dense geometry and camera trajectories directly from image sequences. Traditional approaches (Mur-Artal ct al., 2015; Mur-Artar & Tardós, 2017; Schonberger & Frahm, 2016; Hartley & Zisserman, 2003) are grounded in geometric priors and optimization, but their reliance hand- crafted design choices and iterative solvers often leads to limited scalability and reduced robustness in complex scenes. In contrast, large-scale deep models have shifted the paradigm toward data- driven frameworks, offering remarkable generalization ability across diverse environments (Wang al.. 2025b; Yang et al., 2025). A milestone in this evolution is the Visual Geometry Grounded Transformer (VGGT) (Wang et al., 2025a). This 1.2B-pane -2B-parameter model unifies multiple 3D tasks, including dense depth estimation. point map regression, camera pose prediction, and point tracking within single forward pass, consistently surpassing task-specialized counterparts.
Despite its success, the billion-scale parameterization of VGGT incurs prohibitive computational and memory costs, severely restricting its deployment in real-world scenarios. Model quantiza- tion (Gholami et al., 2022; Jacob et al., 2018) is an effective compression technique by convert- ing weights and activations of model from high-precision foating-pointi to low-precision integers. While this technique has been widely validated in large language models (Frantar et al., 2022; Xiao et al., 2023) and 2D vision models (Yuan et al., 2022; Wu et al., 2024). the quantization of billion- scale 3D reconstruction transformers such as VGGT remains largely unexplored. In our study, we identify two model-specific properties of VGGT that make its quantization partieularly challenging: 1 The presence of data-independent special tokens (camera and register tokens). Unlike reg- ular image tokens that are encoded from input images, these tokens are pretrained and injected into image tokens to encode global context and cross-view geometry. This data-independent property causes activation distributions to deviate from typical patterns, amplifying heavy tails and producing extreme channel and token variance. These skewed statistics are unfriendly to standard quantization, leading to substantial information loss. @ The inherently semantic complexity of 3D data. Each input sequence involves non-identical and complex views, meaning that the underlying semantic and highly redundant. For quantization calibration, the ideal pro-
space is both cess is to perceive the expected major data distribution. If calibration samples are rare outliers and not diverse, the estimated quantization ranges become biased and fail to generalize, causing perfor- mance degradation across unseen scenes. Thus, sample diversity and representativeness are far more critical than in 2D vision tasks.
To address these challenges, present the first systematic investigation of Post- fost-Training Quanti- zation (PTQ) for VGGT and propose a tailored framework, QuantVGGT. Our approach introduces Dual-Smoothed
Quantization (DSFQ), which mitigates skewed statisties by combin- ing (1) a pre-global rotation via Hadamard transforms to disperse outliers and smooth heavy-tailed distributions, and (2) post-local smoothing step that normalizes channel-level variance in the ro- Diverse
tated space. Additionally, to overcome calibration instability, we design Sampling (NFDS), which leverages deep-layer activation statistics to filter noisy extremes and em- ploys frame-aware clustering aligned with VGGT's 's inductive biases. Together, these components yield robust, efficient, and accurate quantization of billion-scale 3D reconstruction transformers.
Our contributions are summarized as follows:
1. We provide the first systematic analysis of PTQ on VGGT, highlighting quantization chal- lenges rooted in its data-independent tokens and multi-view activation statistics.
2. We propose a dual-stage smoothing scheme that globally disperses heavy-tailed distribu- tions and locally balances channel variance, significantly reducing quantization errors.
3. We design a calibration strategy that filters outliers and utilizes VGGT's inductive bias to construct frame-aware clusters, ensuring representative and stable calibration set.
2

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Channel Variance
Global Outlier mitigated
Local Outlier smoothed
Quantization Architecture Design
(Sec. 3.2)
Pre-Rotation X=XH
Post-Smooth X=XHS
Hard to Quant
Mild to Quant
Easy to Quant
Token Variance
Calibiration Dataset Construction (Sec. 3.3)
Ideal Dataset: Major and Diversed (Theorem 3.2)
Outlier Samples
Filter Outlier
Decouple
Free
0
Entangled
Space
Diversed
Figure 2: Overview of proposed QuantVGGT. Top: Our proposed Dual-Smoothed
Quantization architecture. Bottom: Our proposed Noise-Filtered Diverse Sampling strategy.
4. Extensive experiments demonstrate that our approach enables effective low-bit quantization of VGGT, achieving substantial memory and inference efficiency gains while preserving reconstruction accuracy.
2 RELATED WORKS
2.1 LEARNING-BASED 3D RECONSTRUCTION
Thanks to the development of deep learning technology in recent years, 3D reconstruction tasks have gradually shifted from traditional methods (Mur-Artal & Tardós, 2017; Mur-Artue et al., 2015; Schonberger & Frahm, 2016; Hartley & Zisserman, 2003; An al., 2025; Li et al., 2026) that rely heavily on prior knowledge to data-driven learning-based methods. Due the large-scale training process, learning-bast -based methods (Wang et al., 2025b; Yang et al., 2025) often achieve better recon- struction performance and generalization ability. DUSt3R (Wang et al., 2024) predicts the 3D point maps of scene by regressing two RGB images, laying the foundation for learning-based methods. MASt3R (Leroy et al., 2024) further refines the framework by introducing confidence-weightee losses for metric scale approximation. Current model, VGGT (Wang al., 2025a) enables predict- ing camera position, dense depth. point maps, and point tracking with a single forward process. With scaling-up to 1.2B parameters, VGGT achieves ofte-of-thh--rr results across various 3D tasks with even surpasses some task-specifier methods. But up to billions of parameters and enormous compu- tational complexity of VGGT severely limit its widespread deployment and application. However, the compression methods for VGGT, such as quantization, still highly unexplored.
2.2 MODEL QUANTIZATION
Model quantization (Gholami al., 2022; Krishnamoorthi, 1806; Ma et al., 2024a; 2023; 2024b; Feng et al., 2025b) significantly reduces the memory footprint and accelerates inference by reducing the data bit-width. Model quantization can be mainly divided into Quantization-Awan (QAT) and Post-Training Quantization (PTQ). QAT (Jacob et al., 2018; Qin et al., 2020b; Feng al.. 2025a;d) utilizes substantial data train quantization parameters and model weights, thus typically ensuring good performance at extremely low bits. But QAT often requires massive training resources. On the contrary, PTQ (Wei al., 2024; Frantar et al., 2022) only requires little calibration data to fine-tune the quantization parameters, and therefore can be applied to large models. For PTQ. BRECQ (Li et al., 2021) builds the block-wise reconstruction framework, and QDrop (Wei et al., 2022) further enhances the performance by randomly dropping quantization activations. To ensure the effectiveness PTQ on large models, GPTQ (Frantar et al., 2022) utilizes approximate second-order gradient to optimize Large Language Models. To address the impact of imbalanced distribution on quantization, SmoothQuant (Xiao et al., 2023) introduces a smoothing parameter to transfer the difficulty of activation quantization weight. QuaRot (Ashkboos al., 2024) adopts
Training
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similar rotation smooth the distribution. Although these methods perform well on existing 2D- visual and language models, they do not generalize well to large-scale 3D models like VGGT (Wang et al., 2025a). To the best of our knowledge, method is the first PTQ framework specially designed for VGGT, ensuring its performance even at low-bit quantization.
3 METHODS
3.1 PRELIMINARY
3.1.1 VISUAL GEOMETRY GROUNDED TRANSFORMER
Visual Geometry Grounded Transformer (VGGT) (Wang et al., 2025a) is recent architecture de- signed to predict all key 3D attributes from image sequences of arbitrary length. Its core components are tokenization and token registration. For any input sequence I {1 J of N RGB frames, VGGT first tokenizes each frame using a pretrained vision backbone F(-), such as DINOv2 (Oquab et al., 2023), producing
X X= x= [2, F())
24 E Raxd
(1)
where n denotes the token length after patching and d the feature dimension.
To enable multi-attribute reasoning, VGGT augments each frame with one camera token and four register tokens, which are responsible for aggregating different 3D attributes (e-g.. camera parame- ters, geometry). Notably, VGGT introduces two distinct sets of these special tokens: one set & E mgddd is reserved for the first frame, while another set to E gaxd is shared by all subsequent frames. Formally, the token registration process is defined
X { 1 concat(z1, 41), d concat(z,, to)
and the resulting & then forwarded into the VGGT backbone.
3.1.2 POST-TRAINING QUANTIZATION
Quantization (Gholami et al., 2022; Krishnamoorthi, 1806) aims to convert model weights and ac- tivations from floating-point representations into compact low-bit integer formats, thereby reducing both computational cost and memory footprint. Formally, given floating-pon ppaint vector the sym- metric quantization procedure can be described as:
Fint clamp roundd -2N -2N--22-----11111 12)
max (I))
(3)
2N-1-11 111
where N represents the target bit-width, round(.) denotes the rounding operator, and clamp(-) en- sures that the integer values remain within the valid range [-2 2 -11-111]
Among quantization paradigms, Post- post-Training Quantization (PTQ) (Wei et al., 2024; Frantar al., 2022; Feng et al., 2025c) is widely applied for its efficiency. Unlike Quantization-ANae Train- ing (Qin et al., 2020a; Feng et al., 2025a;d), PTQ does not require fine-tuning the weights. Instead, fine-tunes the guantization parameters using only relatively small calibration dataset Dealib. while keeping the original full- preciiicniioon weights fixed. This makes PTQ particularly attractive in real- world deployment where computational resources for fine-tuning are limited.
Following the standard practice in prior works (Yuan et al., 2022; Shang et al., 2023; Feng et al., 2024; Xiao et al., 2023), the quantization enror is typically measured by the following objective:
Lquami = Exnr [1 0 (x()))
(4)
where 01 and 0 denote the full full-precision and quantized model functions, respectively.
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(a) Original distribution.
(b) Registered tokens.
(c) Naive rotation.
(d) Soal-Smotthed.
The motivation and effect Dual-Smoothed Fine-Grained Quantization. (a): Salient distribution of VGGT (Wang et al., 2025a) frame_blar block 9. (b):Saliency of registered tokens. The first five special tokens are more salient. (c): Distribution after naive rotation. (d): Distribution after our dual-smooth. We provide more analysis in Appendix Sec. D.
Figure 3:
3.2
FINE-GRE -REIIRDD QUANTIZATION
Observation VGGT (Wang al., 2025a) exhibits highly skewed numerical distributions, which are amplisied by data-independent tokens (camera and register tokens), leading to substantial quan- tization errors.
As illustrated in Fig. 3b, these data- bata-independent tokens (first tokens) amplify channel and token numerical variance: with massive outliers that much larger than regular patch tokens, producing heavy-tailed distributions. When passed into quantization, these few large elements occupy most of the quantization bins, causing severe numerical distortion (Xiao et al., 2023; Ashkboos al., 2024).
See-Globll-Rotttiee Motivated by rotation-based quantization (Ashkboos et al., 2024; Zhao et al., 2024), we apply random Hadamard transformation spread out the impact of special-token- induced outliers. Given a target dimension d = 2", we can generate the corresponding Hadamard matrix H2 through
11
1
1
11
H2 = 2
-1 and H2. = H2 s0 Hnn-1.
(5)
For dimension that + 2n, we can factorize d = m and have H Hgn @ Hm Follow- ing (Ashkboos et al., 2024: Chee et al., 2023), we denote v as vector containing random draws from (-1,-1) and random Hadamard matrix H Hdiag(e). This random Hadamard ma- trix satisfies H H = I. Given activation X E Bexd and weight W Rdout Xdha, the matrix multiplication invariance preserved as follows:
XW (XH)(WH)".
(6)
Lemma 3.1. Due to the central limit effect. the distribution of values after Hadamard rotation tends approximate Gaussian, thereby smoothing the heavy-tailed distribution introduced by special tokens (Tseng et al., 2024).
Lemma 3.1 suggests that the Hadamard rotation disperses outlier values across channels, resulting in more uniform distribution. thereby significantly reducing their impact. Therefore, the original dis- tribution becomes concentrated and smoother, which is more favorable for quantization. Figure 3c illustrates the smoothed distributions after the Hadamard rotation, where the extremely massive out- liers are mitigated.
Dost-Local-Smoothe Although the Hadamard rotation mitigates global skew, the transformed distri- bution still exhibits considerable local variance, as shown in Fig. 3c. While the Hadamard rotation spreads outliers across channels, it only weakens the global outliers, rather than eliminating the outliers within individual channels. To further reduce quantization error, we introduce channel- wise scale to normalize the internal channel distributions. Unlike traditional scaling (Xiao et al., 2023; Wu et al., 2024) that computes c; = max _]+ we derive scale factors from the rotated distribution, ensuring robustness against extreme special-token values.
max X.H)
é max W 1-
XW
(ddagg WT),
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where balances quantization difficulty between activations and weights (typically a 0.5). This design offers two advantages: (1) the scale factor is derived from a smoother distribution after the pre-rotation, avoiding extreme values that could otherwise complicate weight quantization; and (2) ensures that the post-scaled distribution even smoother. If using pre-scale, the post-rotation would destabilize the benefits of channel-scaling. The scale factors also be fused into neighboring normalization layers (Xiao et al., 2023), introducing no runtime cost.
Fine-Grained Quantization Granularity. The above rotate-and-scale quantization strategy re- duces quantization error by addressing the inner-dimension din. However, the choice of quantiza- tion gramularity also plays a critical role in determining the overall error. Recent studies (Chee al., 2023: Tseng et al., 2024) define the quantization difficulty using the concept of "u-coherent', where for any x, max S ya] | lellr// Vg. with representing the number of elements, where yu represents the quantization difficulty. This suggests that reducing quantization granularity, when feasible, can significantly lower quantization error. From a hardware perspective, as long as the quantized matrix multiplication shares the same quantization parameters across the summation operation, there is no need to convert integers back to floating-point numbers, ensuring efficiency. matrix multiplica- utilize the outer-dimension
tion, only the inner-channel din values summed. Therefore, dous for weight quantization and the token dimension n for activation quantization. In practice, we apply out-dimension-wine wwse quantization the weights and token-wise quantization the activations.
As shown in Fig. 3d, the proposed dual-smoothed fine-grained quantization further reduces the outer-dimension variance in the data distribution, significantly lowering the quantization error, with nearly no additional computational burden (see Appendix Sec. D for efficiency analysis).
3.3 NOISE-FILTERRE DIVERSE SAMPLING
The purpose of the PTQ calibration process is to approximate the behavior of the model in the real data distribution X' using a small calibration set Dealib- Formally, we seek
0g arg min Ex_x [110 0g (1))
(8)
and in practice we approximate the outer expectation with samples from Dealib- Therefore, the calibration set should be statistically representative of X.
Theorem 3.2 (Calibration sampling principle). Suppose X can be divided into different do- mains X'
Xx. Each sub-domain X, has scale V and can be partitioned into N' 2 and
with corresponding scales ple set ,.',, v. where Va stands for the scale of sub-region R4. Considering a constructed sam- (.i.... ,Ik] C. Xx where X* = E(X) denotes expectation input. Denote V as the scale of expected sub-region Ri when 'D satisfies pla; RRG) V for Vay E D, then 'D maximizes the information reflecting X in expectation.
and finite) disjoint sub-regions denoted as
(Vi, ...
We provide the complete proof of Theorem 3.2 in Appendix Sec. A. Theorem 3.2 implies that to construct an effective calibration set we should: (1) partition the data space into meaningful regions (subdomains) and (2) draw samples from each region in proportion to its prevalence. In practical settings where Vs is unknown, a robust strategy is to cluster the dataset into K regions and then sample uniformly inside each cluster (this approximates proportional representation under mild assumptions).
Observation 2. Activations in deeper layers tend to be distinctive, with the majority of samples being highly concentrated, while a few samples are outliers as shown in Fig. 4a.
For the expected distribution, prefer representative distribution while the outliers are spiking samples with minimal density. However, when we divide the subdomains and sample within, the
selected probability of outliers is increased, which disrupts our expected distribution. Therefore, propose first to filter the noisy outliers using deep layer statistics for each candidate sample x; E D:
mij == mean (ayee,(a:)))
8t. == var (layer((x;)) j E L,
99
where L is all used layers union, D is the candidate samples union, layer, (-) denotes the activation in j-th layer. We then compute a noise-score using global robust moments: mean value m.j and variance si.;

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(a) Layer distribution.
(b) Label-Clustered.
(c) Feature-Clustered.
(d) Frame-Clustered.
Figure 4: The motivation and effect of Noise-Filtered Diverse Sampling. (a): Layer distribution
characteristic of VGGT (Wang et al., 2025a). (b): Visualization of label-based clustered samples, colors denote different labels. (c): Visualization
direct feature-based clustered samples, colors denote clusters. (d): Visualization of frame-wise distribution-bai bbasd clustered samples, colors denote clusters. We provide more analysis Appendix Sec.
1 M4
maj+
oj
(ms. M4)² + tE,
D
D
1
Σ
Σ (ii. 12y t
Vj
|D]
8i.j
Jj
|
[D]
(10)
score(r;)
maj
Hj
Si,j - V2
0j
T3
JEL
JEL
where is small constant for numerical stability. We then filter out high-noise samples by thresh- olding the score:
Diltered
{xi E D score(x;;) < ST}.
(1I)
where 7' is set by a percentile (e.g. keep the lowest p% scores). This filtering keeps samples close the "typical"" distribution and removes outliers that would otherwise skew quantization calibration.
Observation Feature clusters based on raw labels are sub-optimal for visual- visual- geometry tasks.
We visualized the distribution of different samples and their corresponding labels in Fig. 4b and Fig. 4c. We found that the feature of these samples highly concentrated and difficult divide, and using labels directly as classification criteria achieves sub-optimal results. Geometry samples are usually complex scene containing multiple objects. Therefore, labels often do directly represent their semantic information. However, we identify that VGGT (Wang et al., 2025a) contains a strong inductive bias: models relative relationship between the first frame and subsequent frames. This motivates structural metric derived from frame-wise features.
Given output feature A Raad of sample x, with = 8 x f (spatial tokens per frame s and f frames). We first reshape A' into frame-wise vectors and construct compact frame-aware corre- lation vector ex E f- by measuring the normalized similarity between the first frame and each subsequent frame:
A'
A' = [ [aj a af.....a/_1]
E /
d: x d.
(a) at)
(12)
c aail2 ||ai122
We then cluster the {c² (c'),,DD. E2 using K-Means to obtain K regions R [RI.... Rx }- According to Theorem 3.2, uniform sampling within each region yields a calibration set that better reflects X. Concretely:

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Published as a conference paper at ICLR 2026
Table Camera Pose Estimation C03Dv2 (Reizenstein al., 2021). Bold: The best result.
Method
Bit Width (W/A)
frames=10
frames=20
AUC 30,
AUC 15,
AUC
3
AUC 30,
AUCWIS,
AUC
AUÇ
Foll Prec.
16/16
89.5
83.2
66.1
54.9
90.0
83.9
67.8
$6.9
RTN
8/8
88.
80.7
60.3
46.7
88.1
80.6
60.2
46.5
BRECQ
QDrop
8/8
88.8
81.8
61.9
49.1
88.5
81.8
61.8
49.1
DepQ-Vi V-T
GPTQ
8/8
89.1
82.6
64.0
52.1
89.1
82.6
63.2
$1.5
SunoothQuant
8/8
89.1
82.5
64.8
53.2
89.1
82.5
64.6
53.1
QuaRot
8/8
89.4
83.0
65.9
54.6
89.4
83.0
66.0
54.7
QuantVGGT
8/8
89,4
66.1
54.8
89.6
83.2
66.0
54,9
RTN
6/6
68.1
80.1
58.1
43.7
88.1
80.2
57.6
43.1
BRECQ
6/6
88.3
80.4
38.7
43.9
83.3
80.4
58.6
43.8
QDrop
6/6
68.5
80.8
58.9
44.1
88.4
80.6
58.8
43.9
DoPQ-ViT
6/6
88.5
80.7
39.4
44.8
83.5
80.7
59.4
44.7
GPTQ
6/6
88.7
61.3
88.7
81.0
46.2
SmoothQuant
6/6
88.8
81.3
61.4
47.4
83.9
81.5
61.5
47.5
QuaRot
6/6
89.0
81.8
62.5
81.9
62.6
49.5
QuantVGGT
6/6
89.2
82.7
65.2
53.8
89.3
82.8
65.6
54.1
77.7
BRECQ
4/4
78.8
65.2
34.1
20.1
78.8
65.3
34.1
20.0
QDmp
35.7
79,2
21.9
DopQ-Vit VIT
4/4
80.3
68.3
38.1
23.3
80.4
68.4
35.5
22.0
RepQ-Vi VIT
GPTQ
4/4
80,5
68.6
38.7
23.2
80.6
68.7
35.6
22.1
AWQ
SmaoothQuntt
4/4
75.4
60.1
25.8
12.3
75.4
60.1
25.5
12.1
QuaRot
QuantVGGT
4/4
86,9
78.7
57,3
43.6
88.2
80.2
58.9
45.1
K
R = {2z E Dattered 0 = k},
Dealib U ((R)), A=1
(13)
where i is the cluster assignment and ?(-)) denotes uniform sampler. This Noise-Filtered Diverse Sampling pipeline reduces the influence of noisy outliers, leverages VGGT's frame-relative induc- tive bias form semantically meaningful clusters shown in Fig. 4d, and yields calibration set that better approximates the true data distribution for PTQ.
4 EXPERIMENTS
4.1 EXPERIMENTAL AND EVALUATION SETTINGS
Evaluation Settings. We select VGGT-IB (Wang 2025a) our base model and conduct all the quantization experiments on it. To validate the effectiveness of our proposed method, we conduct camera pose estimation experiments on CO3Dv2 (Reizenstein al., 2021) and point map estimation experiments DTU (Jensen et al., 2014). For the quantization setting, select two of the most widely studied bit settings W8A8 (8-bit weight and 8-bit activation quantization) and W4A4, as they have better hardware adaptability and bring more acceleration and compression effects (Xiao al., 2023; Ashkboos et al., 2024). More details can be found in Appendix Sec. B.
Baseline Methods. For quantization baseline methods, we adopt the commonly used PTQ baseline To-nn--T--eerstt (RTN), BRECQ (Li et al., 2021), and QDrop (Wei et al., 2022). For 2D-vision transformer baseline, select strong DopQ-ViT (Yang al., 2024) and RepQ-ViT (Li et al., 2023). For language transformer baseline, we select strong GPTQ (Frantar et al., 2022), AWQ (Lin et al., 2024), SmoothQuant (Xiao al., 2023), and QuaRot (Ashkboos al., 2024).
4.2 MAIN RESULTS
Camera Pose Estimation. We conduct camera pose estimation experiments using VGGT-1B (Wang et al., 2025a) on CO3Dv2 dataset (Reizenstein et al., 2021). Following prior works (Wang et al., 2025a), we randomly sample 10 frames for evaluation and further expand to 20 frames for more
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Published as a conference paper at ICLR 2026
generalized evaluation. The results are presented in Tab. 1. Under the relatively simpler W8A8 setting, most quantization methods can maintain relatively good performance but inevitably expe- rience certain performance degradation. Quantvggt preserves 99.9% performance under W8A8, with AUC@30 30 of 89.4 and 89.5 for FP (Full Precision). For the more aggressive W4A4 set- ting, all quantization methods showed significant performance degradation, such as current SOTA method QuaRot (Ashkboos et al., 2024) only achieving 81.6 AUC@30 under 20 frames. While, QuantVGGT still achieved 88.2, maintaining 98% of the model's performance. QuantVGGT can achieve significant performance improvements even under extreme quantization settings compared to existing methods, demonstrating its quantization friendliness towards 3D reconstruction models.
Point Map Estimation. To com- prehensively evaluate the generalized quantization performance of VGGT, we further extend the experiment to the point map estimation task on DTU dataset (Jensen et al., 2014). For evaluation,
Table Point Map Estimation on DTU (Jensen et al., 201 4).
Method
Bit- Bit-Width (W/A)
Acc.
Comp.
N.C.,
Mean
Med.
Mean
Med.
Mean
Med.
Full Prec.
16/16
1.185
0.714
2.232
1.313
0.694
0.779
RIN BRECQ
8/8
1.216
0.730
2.237
1.310
0.687
0.773
sample keyframes every 5 images. The results are pre- sented in Tab. 2. It is worth men- tioning that the calibration dataset all from CO3Dv2 training set, meaning that the DTU data are un- known for the calibration process. However, QuantVGGT still general- izes well on the point map estima- tion task, with even improved metrics compared with the FP model under W8A8. For W4A4 setting, all exist- ing methods show notable performance degradation, like QuaRot with ACC. of only 1.593, While
8/8
1.212
0.725
2.236
1.292
0.690
0.774
QDrop
8/8
1.204
0.720
2.239
1.297
0.692
0.780
DoPQ-ViT
8/8
1,200
0.712
2.235
1.290
0.691
0.783
GPTQ
8/8
1.196
0.721
2.226
1.303
0.688
0.778
SmeothQuant
8/8
1,201
0.719
2.229
1.281
0.692
0.776
QuaRot
8/8
1.184
0.712
2.231
1.311
0.694
0.778
VuGntVGGT
8/8
1,182
0.710
2.215
1.276
0.700
0.788
RTN
4/4
1,700
0.930
2.028
1.099
0.656
0.757
BRECQ
4/4
1.688
0.924
2.024
1.082
0.662
0.762
QDrop
4/4
1.642
0.912
2.012
1.073
0.673
0.754
DopQ-ViT GPTQ
4/4
1.587
0.906
2,003
1.075
0.673
0.755
4/4
1.422
0.899
1.997
1.068
0.675
0.761
SmeothQuant
4/4
1,740
0.944
1.993
1.083
0.675
0.764
QuaRit
4/4
1.593
0.916
2.034
1.096
0.670
0.757
QuantVGGT
4/4
1.282
0.743
1.992
1.068
0.681
0.774
QuantVGGT achieves ACC. of 1.282, significantly closer to the FP performance of 1.185. This demonstrates QuantVGGT's ability to adapt to large 3D models such as VGGT quantization, and maintain strong generalization ability with efficient PTQ process.
Point Cloud Reconstruction. To fur- ther demonstrate the generalization abil- ity of QuantVGGT beyond Co3Dv2 and DTU, we conduct additional point cloud reconstruction experiments on the 7-Scenes (Shotton
Table 3: Point Cloud Reconstruction experiment on VGGT (Wang et al., 2025a) using 7-Scenes (Shotton et al., 2013) and NRGBD (Azinović et al., 2022b) dataset.
al., 2013) and NRGBD (Azinović et al., 2022b) datasets under the same W4A4 quantization pro- tocol. The results are in Tab. 3. On both datasets, QuantVGGT substan- tially outperforms current SOTA methods SmoothQuant and QuaRot, also maintains nearly lossless performance. These re- sults demonstrate that QuantVGGT retains strong reconstruction quality across di- verse benchmarks, highlighting its robust- ness and generality.
Method
Acc.j
Comp.y
N.C.,
Meun
Med.
Mean
Med.
Mean
Med.
7-Scenes
Full Prec.
0.025
0.013
0.036
0.020
0.728
0.836
SmoothQuant
0.370
0.261
0.498
0.361
0.484
0.477
QuaRot
0.030
0.016
0.042
0.022
0.701
0,800
QuantVGGT
0.026
0.013
0.037
0.019
0.718
0.812
NRGBD
Full Prec.
0.015
0.009
0.017
0.007
0.878
0.969
SmoothQuant
0.479
0.393
0.614
0.489
0.515
0.513
QuaRot
0.034
0.021
0.030
0.015
0.820
0.948
QuantVGGT
0,019
0.013
0.021
0.010
0.850
0.959
4.3 ABLATION STUDY
To validate the effectiveness of each proposed component, we conduct an ablation study. All the experiments are conducted under W4A4 quantization setting CO3Dv2 (Reizenstein et al., 2021).
Quantization Architecture. We first validate the proposed Dual- mnal-Smoothed Fine- Grained Quanti- zation (DSFQ) and present the result in Tab. 4. We denote naive quantization without any smoothing as Base. We further compare with the rotation-only (Rotation) and scale-only (Scale) methods with our proposed DSFQ Naive quantization shows significant performance collapse with AUC@3 of

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Published as a conference paper at ICLR 2026
only 9.7. While scale-based and rotation- based methods further smoothed data distribution and showed certain improvement, they still exhibit inevitable degradation.
While our proposed DSFQ combines the ad- vantages of both rotation and scale and utilizes fine-grained quantization granularity, greatly preserves the performance. Furthermore, to validate the necessity of our pre-global-rotation and post-local-smooth (Rot.-Scale), we also compared with pre-smooth and post-rotation (DSFQ). In terms of performance, our DSFQ approach has indeed achieved significant im- provements compared to Scale-Rot. This proves that the effect of smoothing the rotated space is more stable, while smoothing first and then rotating will weaken the influence of smoothing due to its rearrangement of outliers between chan- nels, demonstrating the effectiveness of our method.
Table 4: Ablation study on quantization architec- ture.
Method
AUC 30
AUC
AUC&S,
AUC
Full Prec.
89.5
83.2
66.1
54,9
Base
76.9
61.5
23.9
9.7
83.6
72.3
46.3
32.5
Soale
81.9
70.1
38.5
21.2
Soale-Rot.
86.7
78.5
56.8
42,9
DSFQ
86.943.a
78.7.-6.4
57.3+11.0
43.6.-11
Sampling Strategy. We then validate the proposed Noise- Filtered Diverse Sampling (NFDS) and show the result in Fig. 5. We denote the naive random sampling strategy as Ran- dom. We then compare with random sampling from outlier- filtered dataset (Filtered) and sampling from frame-based clus- tered dataset without filtering (Clustered) strategy. All the results are conducted under five different random seeds. We present the mean performance and its corresponding variance in the bar plot. Random selection not only fails guarantee diversity but also results in significant variance due to the in- fluence of outliers. The filtered data quality was improved, and the variance was significantly reduced. Our clustering method significantly enhances diversity and improves average performance, but there is still variance due to the presence of outliers. The final combined NFDS ensures both the removal of outliers and well diversity, ensuring average performance while being more stable.
86.910.22
86.5 85.420.96 86.010.43 0.43
84.5
$4.0
Random
Fitered
Olustered
NFD5
Figure
Sample strategy abla- tion.
4.4 EFFICIENCY ANALYSIS
To verify the deployment efficiency of quantized VGGT, report the hardware latency in Fig. 6. All the experiments are conducted on a single NVIDIA RTX 4090 24G GPU with CUDA 12.4. We CUT- LASS (Thakkar et al., 2023) on top of PyTorch for per- forming low-bit INT matrix multiplication. Compared with naive quantization without any smooth techniques, our hiual-smoothed fine-grained quantization only brings additional 0.2% latency cost at W4A4, while signifi- cantly preserving the quantized model performance. Our W4A4 QuantVGGT even surpass the naive W8A8 per- formance, and with a significant performance gap naive W4A4. This indicates that our VGGT- VGGT-specialized quantization scheme greatly outperforms the existing naive guantization with little extra burden.
Latency
AU0830
2.10
090.0
90
65.8
86.2
1.40
Latenoy
1.00
0.457x
0.461x
0.400x
0.402x
0.70
68
0.35
00
FP16
Neive WSAS
QuentVOOT WBAB
WAA4
Neive
QuantVGGT
W4A4
66
Figure 6: Latency speedup and corre- sponding performance under 20 frames.
5 CONCLUSION
In this paper, we propose the first Post-Training Quantization (PTQ) framework for VGGTs, namely Quan Specifically, we identify the quantization-unfriene distribution brought by data- independent tokens and the highly unstable calibration dataset inherent in 3D multi-view data. We then propose Dual- Smoothed Grained Quantization to smooth the heavy-tailed distribution. We also design Noite-Filtered Diverse Sampling to constructs frame-aware diverse calibration clusters to ensure stable dataset. Extensive experiments demonstrate that Quant achieves state-of-the- art performance under different bit-widths and greatly surpasses existing quantization methods.
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6 ACKNOWLEDGEMENTS
This work is supported by the National Natural Science Foundation China under Grant Number 62476264 and 62406311 12, the Beijing Natural Science Foundation under Grant Number 4244098, the Science Foundation of the Chinese Academy of Sciences, and the Swiss National Science Founda- tion (SNSF) project 200021E 219943 Neuromorphic Attention Models for Event Data (NAMED).
ETHICS STATEMENT
This research strictly adheres the ICLR Code of Ethics with no ethics-related risks: it uses pub- lic open-source models (VGGT (Wang et al., 2025a)) and focuses on algorithmic innovation for inference acceleration and compression, without involving scenarios endangering public safety, fringing privacy, or producing discrimination.
REPRODUCIBILITY STATEMENT
To ensure reproducibility, experimental configurations, method details, and evaluation metrics thoroughly described Sec. 4.1 and Appendix Sec. B. Experimental results of comparative methods sourced from public literature, and experiments strictly follow the same configurations as baseline methods for fair comparison. Complete source code for reproducing results will be publicly released upon paper publication. The raw reconstruction files attached in the supplementary materials. For the theorem used in the paper, we also provided a detailed proof in Appendix Sec. A.
REFERENCES
Zhulin An, Xinqiang Yu, Chu Wang, Yinlong Zhang, and Chunhe Song. Em- bodied intelligence: Recent advances and future perspectives, 2025. ISSN 3105-
8515. URL https: /wwp://wwwwww/w./// the- inw.iiniiioonnoomm wwweettiiooonnngiiiioeoo 6lda7Bbleeeddd04112000..
Salch Ashkboos, Amirkeivan Mohtashami, Maximilian L Croci, Bo Li, Pashmina Cameron, Martin Jaggi. Dan Alistarh, Torsten Hoefler, and James Hensman, Quarot: Outlier-free 4-bit inference in rotated llms. arXiv preprint arXiv:2404.00453 00456, 2024. 3, 5, 8, 9, 16, 18, 21
Dejan Azinović, Ricardo Martin-Brualla, Dan B Goldman, Matthias Nießner, and Justus Thies. Neural rgb-d surface reconstruction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6290-6301, 2022a. 16
Dejan Azinović, Ricardo Martin-Brualla, Dan B Goldman, Matthias Nießner, and Justus Thies. Neural rgb-d surface reconstruction. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6290-6301, 2022b. 9
Jerry Chee, Yaohui Cai, Volodymyr Kuleshov, and Christopher De Sa. Quip: 2-bit quantization of large language models with guarantees. Advances in Neural Information Processing Systems, 36:4396 4429, 2023.
Weilun Feng, Chuanguang Yang, Zhulin An, Libo Huang, Boyu Diao, Fei Wang, and Yongjun Xu. Relational diffusion distillation for efficient image generation. In Proceedings of the 32nd ACM International Conference on Multimedia, pp. 205-213, 2024.
Weilun Feng, Haotong Qin, Chuanguang Yang, Zhulin An, Libo Huang, Boyu Diao, Fei Wang, Renshuai Tao, Yongjun Xu, and Michele Magno. Mpq-dm: Mixed precision quantization for extremely low bit diffusion models. In Proceedings of the AAAI Conference on Artificial Intelli- gence, volume 39, pp. 16595-16603, 2025a. 3.4
Weilun Feng. Haotong Qin, Chuanguang Yang, Xiangqi Li, Han Yang, Yuqi Li, Zhulin An, Libo Huang, Michele Magno, and Yongjun Xu. vdtt Accurate quantized video diffusion trans- former with salient data and sparse token distillation. arXiv preprint arXiv: 25080060 2025b.
11

---

Published as a conference paper at ICLR 2026
Weilun Feng, Chuanguang Yang, Haotong Qin, Xiangqi Li, Yu Wang. Zhulin An, Libo Huang, Boyu Diao, Zixiang Zhao, Yongjun Xu, et al. Q-vdit: Towards accurate quantization and distillation of video-generation diffusion transformers. In International Conference Machine Learning, pp- 16956-16976. PMLR, 2025c. 4
Weilun Feng, Chuanguang Yang, Haotong Qin, Yuqi Li, Xiangqi Li, Zhulin An, Libo Huang, Boyu Diao, Fuzhen Zhuang, Michele Magno, et al. Mpq-dmv2: Flexible residual mixed precision quan- tization for low-bit diffusion models with temporal distillation. arXiv preprint arXiv:2507.04290, 2025d. 3,
Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh. Gptq: Accurate post-training quantization for generative pre-trained transformers. arXiv preprint arXi::2210.17923, 2022. 2, 3,4,
Amir Gholami, Sehoon Kim, Zhen Dong, Zhewei Yao, Michael W Mahoney, and Kurt Keutzer. A survey of quantization methods for efficient neural network inference. In Low-Power Computer Vision, pp. 291-326. Chapman and Hall/CRC, 2022. 2, 3,
Richard Hartley and Andrew Zisserman. Multiple view geometry in computer vision. Cambridge university press, 2003. 2,
Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko. Quantization and training of neural networks for inference. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2704-2713, 2018. 2,3
efficient
Rasmus Jensen, Anders Dahl, George Vogiatzis, Engin Tola, and Henrik Aanæs. Large scale multi- view stereopsis evaluation. In Proceedings the IEEE conference on computer vision and pattern recognition, pp. 406-413, 2014. 8,
Raghuraman Krishnamoorthi. Quantizing deep convolutional networks for efficient inference: A whitepaper. arxiv 2018. arXiv preprint arXiv: 1806.0834,, 1806. 3, 4
Vincent Leroy, Yohann Cabon, and Jérôme Revaud. Grounding image matching in 3d with mast3r. In European Conference on Computer Vision, pp. 71-91. Springer, 2024.
Yuhang Li, Ruihao Gong. Xu Tan, Yang Yang, Peng Hu, Qi Zhang, Fengwei Yu, Wei Wang, and Shi Gu. Breeq: Pushing the limit of post-training quantization by block reconstruction. arXiv preprint 102.v:22022.00
2021. 3, 8, 16, 20
Yuqi Li, Siwei Meng, Chuanguang Yang, Weilun Feng, Junming Liu, Zhulin An, Yikai Wang, and Yingli Tian. A comprehensive survey of interaction techniques in 3d scene generation. Authorea Preprints, 2026.
Zhikai Li, Junrui Xiao, Lianwei Yang, and Qingyi Gu. Repq-vit: Scale reparameterization for post-training quantization of vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 17227-17236. 2023. 8
Lin, Jiaming Tang, Haotian Tang, Shang Yang, Wei-Ming Chen, Wei-Chen Wang, Guangxuan Xiao, Xingyu Dang. Chuang Gan, and Song Han. Awq: Activation-aware weight quantization for on-device IIm compression and acceleration. Proceedings of machine learning and systems, 6:87-100, 2024. 8
Yuexiao Ma. Taisong Jin, Xiawu Zheng, Yan Wang, Huixia Li, Yongjian Wu, Guannan Jiang, Wei Zhang, and Rongrong Ji. Ompq: Orthogonal mixed precision quantization. In Proceedings of the AAAI conference on artificial intelligence, volume 37, pp.
2023.
Yuexiao Ma, Huixia Li, Xiawu Zheng, Feng Ling, Xuefeng Xiao, Rui Wang. Shilei Wen, Fei Chao, and Rongrong Ji. Affinequant: Affine transformation quantization for large language models. arXiv preprint Xrxi 240.2424../44. 2024a.
Yuexiao Ma, Huixia Li, Xiawu Zheng, Feng Ling, Xuefeng Xiao. Rui Wang. Shilei Wen, Fei Chao, and Rongrong Ji. awwre slicing for post-training quantization in vision transformer. In Forty-first International Conference Machine Learning, 2024b. 3
12

---

Published as a conference paper at ICLR 2026
Raul Mur-Artal and Juan D Tardós. Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras. IEEE transactions on robotics, 33(5):1255- 1262,, 2017. 2,
Raul Mur-Artal, Jose Maria Martinez Montiel, and Juan Tardos. Orb-slam: A versatile and accurate monocular slam system. IEEE transactions roboties, 31(5):1147-1163 2015. 2, 3
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Mare Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint wrxiv:2304.07193, 2023, 4
Haotong Qin, Ruihao Gong, Xianglong Liu, Mingzhu Shen, Ziran Wei. Fengwei Yu, and Jingkuan Song. Forward and backward information retention for accurate binary neural networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2250- 2259, 2020a. 4
Haotong Qin, Ruihao Gong, Xianglong Liu, Mingzhu Shen, Ziran Wei, Fengwei Yu, and Jingkuan Song. Forward and backward information retention for accurate binary neural networks. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pp. 2250- 2259, 2020b.
Jeremy Reizenstein, Roman Shapovalov. Philipp Henzler, Luca Sbordone, Patrick Labatut, and David Novotny. Common objects in 3d: Lcrge-scale learning and evaluation of real-life 3d cat- egory reconstruction. In Proceedings of the IEEF/CVF international conference on computer vision, pp. 10901-10911, 2021. 8, 9, 16, 18, 19
Johannes L Schonberger and Jan-Michael Frahm. Stracture-from-motion revisited. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4104-4113, 2016. 2,
Yuzhang Shang, Zhihang Yuan, Bin Xie, Bingzhe Wu, and Yan Yan. Post-training quantization on diffusion models. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1972-1981, 2023. 4
You Shen, Zhipeng Zhang, Yansong Qu, and Liujuan Cao. Fastvggt: Training-fres free acceleration of visual geometry transformer. arXiv preprint arXiv:2509.02560 2025. 17
Jamie Shotton, Ben Glocker, Christopher Zach, Shahram Izadi, Antonio Criminisi, and Andrew Fitzgibbon. Scene coordinate regression forests for camera relocalization in rgb-d images. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2930-2937, 2013. 9, 16, 17
V. Thakkar et al. CUTLASS, 2023, URL https: WBLLLttss//ppttbb....///W/////// wut.DDDDACcutas......... 10
Albert Tseng, Jerry Chee, Qingyao Sun, Volodymyr Kuleshov, and Christopher De Sa. Quipit: Even better I1m quantization with hadamard incoherence and lattice codebooks. arXiv preprint arXiv:2402.044 04396. 2024. 5, 6, 16
Jannyuan Wang, Christian Rupprecht, and David Novotny. Posediffusion: Solving pose estimation via diffusion-aided bundle adjustment. In Proceedings of the IEEE/CVF International Conference
on Computer Vision, pp. 9773-9777 -7733, 2023. 16
Jianyuan Wang, Minghao Chen, Nikita Karaev, Andrea Vedaldi, Christian Rupprecht, and David Novotny. Vggt: Visual geometry grounded transformer. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 5294 5306, 2025a. 1, 4, 5, 7, 8, 9, 11, 16, 17, 18
Qianqian Wang, Yifei Zhang, Aleksander Holynski, Alexei Efros, and Angjoo Kanazawa. Con- tinuous 3d perception model with persistent state. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 10510-10522, 2025b. 2,3
Shuzhe Wang. Vincent Leroy, Yohann Cabon, Boris Chidlovskii, and Jerome Revaud. Dust3r: Ge- ometric 3d vision made easy. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 20697-20709, 2024. 3, 16
Lu Wei, Zhong Ma, Chaojie Yang, and Qin Yao. Advances in the neural network quantization: A comprehensive review. Applied Sciences, 14(17):7445, 2024. 3,
13

---

Published as a conference paper at ICLR 2026
Xiuying Wei, Ruihao Gong, Yuhang Li, Xianglong Liu, and Fengwei Yu.
Qdrop: Ran- arXiv preprint
domly dropping quantization for extremely low-bit post-training quantization. arXiv:2203..5570, 2022. 8, 16
Junyi Wu, Haoxuan Wang, Yuzhang Shang, Mubarak Shah, and Yan Yan. Ptq4dit: Post-training quantization for diffusion transformers. arXiv preprint arXiv:2405. 16005, 2024. 2,5
Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, and Song Han. Smoothquant: Accurate and efficient post-training quantization for large language models. In International Conference Machine Learning, pp. 38087-38099, PMLR, 2023. 2, 3, 4, 5, 6, 8, 16, 18
Jianing Yang, Alexander Sax, Kevin J Liang, Mikael Henaff, Hao Tang. Ang Cao, Joyce Chai, Franziska Meier, and Matt Feiszli. Fast3r: Towards 3d reconstruction of 1000+ images in one forward pass. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp- 21924-21935, 2025. 2, 3, 16, 17
Lianwei Yang, Haisong Gong, Haokun Lin, Yichen Wu, Zhenan Sun, and Qingyi Gu. Dopq Towards distribution friendly and outlier-aware post-training quantization for vision transformers. arXiv preprint arXiv:2408.0332)) 2024. 8
Zhihang Yuan, Chenhao Xue, Yiqi Chen, Qiang Wu, and Guangyu Sun. PtqAvit: Post-training quantization for vision transformers with twin uniform quantization. In European conference on computer vision, pp. 191-207. Springer, 2022. 2,4
Tianchen Zhao, Tongcheng Fang, Enshu Liu, Wan Rui, Widyadewi Soedarmadji, Shiyao Li, Zinan Lin, Guohao Dai, Shengen Yan, Huazhong Yang, et al. Vidit-q: Efficient and accurate quantiza- tion of diffusion transformers for image and video generation. arXiv preprint arXiv:2406.0255: 02540, 2024. 5
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A PROOF OF THEOREM 3.2
Proof. For X E(X), have N max(NN). Since N is finite, N is also finite. And for the scale V of sub-region R. of X", we have Vi- (V)), then we have Vjj V2 for Vi,j.
Therefore, v VV V*/N".
Consider D {To, oK} C X", to maximize the information D, we need maximize:
N
max H(D) Σ
E pla, E log Rj).
(14)
2m
Since all samples are independent, Eq. 14 is equivalent to
EH,(D)= Σ
pln E R2) logpla R2)).
(15)
Without loss of generality, we only need to discuss max H.(D). To simplify writing, we denote p(a, E R2)) as Pjs and the above problem can be derived
N pj log
max HD( max
Σ
(16)
To solve this problem, we introduce Lagrangian multiplier and construct Lagrangian as:
N ,P.. A) = Pj logp; +
N'
-1
C(p.. ... ..
Σ ==1
Σ Pj 1=1
-1
(17)
can solve this by letting:
N
OL
0,
P5
(18)
Öp;
j=1
Therefore, we have:
ac 0p,
(logp; + +1) + = = Pj = -
(19)
Substitute into Σ Pj = 1:
N A-1
Pj
(20)
N
N
At this point, H.(x") = log N* (maximum entropy).
Given that Vj, VJ+ V* V*/N*, when p; = Vi N-. the information is maximized. Therefore, Theorem 3.2 holds.
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B EXPERIMENT SETTINGS
For camera pose estimation, we randomly select 10 frames and 20 frames to validate the performance under varying sequence lengths following (Wang al., 2025a; 2023). We use the standard metric AUC (Wang et al., 2023), which combines RRA (Relative Rotation Accuracy) and RTA (Relative Translation Accuracy). For point map estimation, we sample keyframes every 5 images. Consistent with prior works (Azinović et al., 2022a; Wang et al., 2024), we use Accuracy (Acc.), Completion (Comp.), and Normal Consistency (N.C.) to validate the performance.
Same with prior works (Li et al., 2021; Xiao et al., 2023), we adopt channel-wise weight quantization strategy. For GrninGr Quantization Granularity, we further use dynamic moken-wise quantiza- tion for activation. We use symmetry quantization for both weight and quantization for efficiency. For Hadamard rotation, we use random Hadamard matrix following (Ashkboos et al., 2024; Tseng et al., 2024). For the calibration process, use block-wise quantization pipeline following (Li et al., 2021; Wei et al., 2022).
For hyperparameter setting. set 0.5 in Eq. 7 for channel-wise scale initialization. We set p = 0.2 Eq. 1l and cluster center K in Eq. 13 for calibration dataset construction. We select 40 samples from total 400 samples pool. During calibration process, set channel-wise scale è and quantization parameters A as learnable. We set the learning rate as 5e for and 5e for A.
Table 5: More quantization setting results on CO3Dv2 (Reizenstein et al., 2021) under W4A4.
Method
AUC 30,
AUC@ 15,
AUC@S;
AUC@3,
Prec.
90.5
84.4
67.9
57.0
QuaRot
71.4
41.0
24.4
QuantVGGT
87.9
79.0
58.4
44.7
QuantVGGT (Mixed Precision)
88.0
78.8
58.4
44,6
QuantVGGT (Asymmetricc
87.8
78.9
58.6
44.8
QuantVGGT (QAT)
Table Point Cloud Reconstruction experiment on Fast3R (Yang et al., 2025) using 7-Scenes (Shot- ton et al., 2013) dataset.
Acc.j
Comp.
N.C.t
Method
Mean
Med.
Mean
Med.
Mean
Med.
Full Prec.
0.049
0.021
0.069
0.021
0.624
0.683
SmoothQuant
0.497
0.448
0.319
0.244
0.586
0.625
QuaRot
0.312
0.258
0.149
0.080
0.593
0.637
QuantVGGT
0.049
0.022
0.070
0.022
0.624
0.683
Table Camera Pose Estimation comparison with other acceleration methods on C03Dv2 (Reizen- stein al., 2021)
Method
(T))
AUC 30,
AUC@#15;
AUC
AUC
VGGT
5.84
89.5
83.2
66.1
54.9
FastVGTT
1.68
82.7
71.3
39.2
25.1
QuantVGGT
1.40
86.9
78.7
57.3
43.6
C MORE EXPERIMENTS RESULTS
We further evaluate QuantVGGT under mixed precision and asymmetric quantization under W4A4 on CO3Dv2. We use the first 200 sequences of each class for faster evaluation. Results are present in Tab. 5. These results indicate that QuantVGGT is not tied to specific quantization format and
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Table Ablation study on activation quantization granularity under W4A4.
Compute
Granularity
Memory Opt.
Latency Opt
AUC@30. 30-
Full Prec.
Full Prec.
1.00x
1.00x
89.5
Static
Tensor-wise
3,65x
2.50x
82.2
Static Token-wise
3.65x
2.50x
84.1
Dynamic Tensor-wise
3.65x
2.49x
82.7
Dynamic
Token-wiid wise
3.65x
2.49x
86.9
can be readily deployed under accuracy. Due the limited computation resources but to still provide empirical evidence, we conduct a lightweight QAT experiment by fine-tuning the quantized model for epoch on the same calibration dataset used by QuantVGGT (i.e., no extra are introduced). The results are in Tab. 5. QAT indeed improves low-bit performance, but the computational overhead is substantial.
asymmetric settings while maintaining strong
To validate QuantVGGT generality across architectures, we additionally evaluate VunntVGGT on Fast3R (Yang et al., 2025), recently proposed transformer-basee 3D reconstruction backbone. We report point-cloud reconstruction results on 7-Scenes (Shotton et al., 2013) dataset in Tab. 6. QuantVGGT achieves practically lossless performance relative to the full full-precision Fast3R model and substantially surpasses strong quantization baselines (QuaRot and SmoothQuant) on all metrics.
We further provide comparison with other VGGT (Wang et al., 2025a) acceleration methods. FastVGGT (Shen et al., 2025) accelerates VGGT via token compression/token merging, while our conceptually orthogo-
QuantVGGT focuses post-training quantization. These techniques nal and, in principle, can be combined. To provide a concrete comparison, we evaluate run- time FLOPs and reconstruction accuracy on Co3Dv2 in Tab. 7. From these results, observe that: Quan achieves larger FLOPs reduction than FastVGGT (1.40T vs. 1.68T), while also maintaining significantly higher accuracy all metrics. This highlights that quantization- acceleration (QuantVGGT) can be highly competitive with woweenooomesssioo---.. based acceleration (FastVGGT), even though they target different aspects of the model.
(a) frame block7.
(b) frame block8.
(c) global-block? block7.
(d) global block8.
(c) frame block7.
( frame_ block8.
(g) global block?.
(h) global block8.
Figure More salient distribution and registered tokens saliency.
D MORE ANALYSIS ON DUAL- FINE-GRAINED QUANTIZATION
In this section, we provide more analysis on the proposed Pual-Smoothel Fine-Grained Quantiza- tion (DSFQ).
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We first present more visualizations of the heavy-tailed activation distribution of VGGT (Wang et al., 2025a) and the amplified salient phenomenon brought by data-indeepndent tokens in Fig. 7. can be seen that this salient phenomenon is reflected in different layers of VGGT, which will bring universal bottlenecks to the quantization performance. And these data-independent tokens almost always have more severe salient phenomena than other adjacent image tokens, which reflects the salient amplified effect of these special registration tokens.
Also, verify the trade-off between performance and latency in our fine-grained quantization, tested the impact of different quantization granularities on latency and performance, and present the results in Tab. 8. For weight quantization, we apply uniform channel-wise quantization. Here, we only study activation quantization granularity. 'Static' compute denotes computing the quantization parameter A Eq. during calibration and fixed during inference. 'Dynamic' denotes computing A during inference based on min-max value. It can be seen that dynamic quantization and token- wise quantization impose almost no burden on memory and only result in an additional lateney of 0.01 % when used simultaneouslly However, this fine-grained quantization brings significant performance improvements. Compared to static tensor-wise quantization, dynamic token-wise quantization only brings 0.01 % additional latency but improves AUC@ 30 from 82.2 to 86.9.
We also validate the proposed scaling and Hadamard based rotation inference overload under W4A4. The results shown in Tab. 9, For the Hadamard transform, we follow existing work QuaRot (Ashkboos et al., 2024) that apply ment. For the smooth factor, follow SmoothQuant (Xiao al., 2023) that fuses the factor within previous LayerNorm operation, which introduces no extra inference burden. With fast-hadamard- transform CUDA kernel, the Hadamard transform almost introduce no extra burden but significantly improves model performance form 81.9 AUC@ 30 to 86.9. This proves both the efficiency and effectiveness of the proposed DSFQ pipeline.
with CUDA kernel for deploy-
Table 9; Inference overload of proposed quantization preprocess operations under W4A4.
Method
Memory Opt.;
Latency Opt.
AUC 30,
QuantVGGT (w/ Hadamard) QuantVGGT
3.65
81.9x 2.50x 2.49x
3.65
86.9x
We further conduct an ablation study on Hadamard matrix and smooth factor. For Hadamard matrix, it is randomly generated, we use different random seed for ablation. And we vary smoothing a for sensitivity analysis. Here, we present the results in Tab. 10. These results indicate that QuantVGCN hyperparameter
rely on heavily tuning and is robust to small perturbations.
Table 10: Ablation study of Hadamard and smooth factor sensitivity on CO3Dv2 (Reizenstein et al., 2021) under W4A4.
Method
AUC 30,
AUC@ 15,
AUC@S,
AUC@3 3,
Full Prec.
89.5
83.2
66.1
54.9
QuaRot
81.8
70.3
40.1
23.5
QuantVGGT (seed 0.5)
86.9
78.7
$7.3
43.6
QuantVGGT (seed 1, 0.3)
86.6
78.4
$7.0
43.3
QuantVGGT (seed 1, 0.7)
86.5
78.3
57.0
43.2
(seed QuantVGGT
0.5)
57.1
43.5
QuantVGGT (seed 3, 0.5)
86.9
78.7
57.3
43.7
E MORE ANALYSIS ON NOISE-FILTERED DIVERSE SAMPLING
In this section, we provide more analysis of our proposed Noise-Filtered Diverse Sampling (NFDS).
We conduct ablation study on threshold cluster number K, and calibration sample size N. The results (W4A4 VGTT on Co3Dv2, 10 frames) are summarized Tab. 11. These results show that QuantVGGT is robust over wide range of hyperparameters, and performance steadily improves with more calibration samples.
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Table 11: Ablation study of NFDS hyperparameters on CO3Dv2 (Reizenstein al., 2021) under W4A4.
Method
AUC@30;
AUC@IS,
AUC@S;
AUC@3;
Full Prec.
90.5
84.4
67.9
57.0
QuaRot
82.8
71.4
41.0
24.4
Calibration Size
QuantVGGT (5 samples)
86.4
77.6
53.8
38.9
QuantVGGT (20 samples)
86.9
78.4
55.3
40.8
QuantVGGT (20 samples)
86.9
55.3
QuantVGGT (40 samples)
87.9
79.0
58.4
44.7
Filter Threshold
QuantVGGT (p pp=00.)
87.5
78.7
57.8
44.2
QuantVGGT (p == 0.2)
87.9
79.0
58.4
44.7
QuantVGGT (p- 0.3)
87.5
78.8
57.8
44.3
Cluster Number
QuantVGGT [K 6)
87.7
79.7
58.1
44.4
QuantVGGT [K 8)
87.9
79.0
58.4
44.7
QuantVGGT (K 10)
87.7
79.7
57.9
44.0
675
09.3:0.79
We present quantization performance using different cluster strate- gies using five random seeds. We denote our NFDS Frame- based, directly using features to cluster as Srature-based, and us- ing prior labels cluster as Label-based. The performance com- parison is presented in Fig. 8. It can be seen that the experimental results are consistent with our previous analysis. The dataset con- structed based on inductive bias clustering results can bring better average performance and significantly reduce the impact of ran- domness. However, datasets constructed using other prior knowl- edge have only slight improvements compared to completely ran- dom ones.
87.0
86.210.41
83.420.95
85.0
Figure 8: More ablation study
sample strategy.
To evaluate NFDS under extreme cases, we construct a subset from CO3Dv2 (Reizenstein et al., 2021) dataset where the first frame significantly inconsistent with the rest of the sequence. Specif- ically, we filter calibration candidates whose average cosine similarity between the first frame and all other frames
0.1. On this challenging subset, we compare Random sampling and NFDS using 20 samples and present the results in Tab. 12. NFDS consistently outperforms random sampling be- cause: Outlier filtering removes extreme low-quality samples before clustering; Frame-awan clus- tering is not solely dependent the first frame, since clustering focuses relative inter-frame geometry, rather than absolute appearance quality.
Table 12: Ablation study of calibration data sampling strategy on CO3Dv2 (Reizenstein et al., 2021)
extreme cases under W4A4.
Method
AUC@30,
AUC@15,
AUC@5,
AUC@3,
Full Prec.
90.5
84.4
67.9
57.0
Random
87.7
78.6
57.6
43.4
NFDS
87.9
79.0
58.4
44.7
F CALIBRATION AND INFERENCE COMPUTATION RESOURCE
In this section, report the calibration and inference computation resource and the additional burden brought by our proposed methods. We first present the inference comparison Tab. 14 We present the detailed results in Tab. 13 and the performance under W4A4. The filter and cluster
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Table 13: Ablation study calibration costs.
Calibration Overload
Performance
Method
GPU Memory (GB),
GPU Time (Hours),
AUC 30.
AUC@155
Full Prec.
90.0
83.9
Naive PTQ
14.4
2,53
78.8
65.2
QuantVGGT filee
0.2
j0.0
17.4
77.1 111.9
QuantVGGT who cluster
14.6:0.2
2.64.40.11
86.0
76.5.
QuantVGGT
14.6 0.2
2.67 (0.14
86.9 9.0
78.7 (14.9
are used in calibration dataset construction and we select 40 samples from 400 data pool to ensure the robustness. Compared to the baseline PTQ process (Li et al., 2021), QuantVGGT only brings an additional memory consumption of 0.02GB and an additional calibration time of 0.1 hour, but brings significant performance improvement. And the additional calibration time is almost only affected by the construction of the calibration dataset. But even our complete sampling strategy only brings an additional 0.14 hours of time, and the total PTQ process only takes 2.67 hours and can be performed on consumer GPUs such as RTX40900. This fully demonstrates that our PTQ algorithm is highly efficient and effective.
Table 14: Efficiency comparison across memory and latency under different bit-width.
Bit-width (W/A)
Memory Opt.,
Latency Opt.
16/16 8/8 (naive)
1.00x
1.00x
1.94x
2.19x
8/8 (ours)
1,93x
2.17x
4/4 (ours)
3.65x
2.49x
To better illustrate the practical acceleration benefits of our QuantVGGT under varying compu- tational demands, we have conducted an additional ablation experiment measuring the inference acceleration of QuantVGGT (W4A4) at different sequence lengths. The detailed latency optimiza- tion is shown Tab. 15. The results show that even as sequence length increases, QGTT consistently provides substantial acceleration with stable scaling trends.
Table 15: Latency optimization of W4A4 Quant under different input sequence lengths.
Length
10
20
40
80
Latency Optimization
2.47x
2.49x
2.53x
2.55x
G THE USE OF LARGE LANGUAGE MODELS (LLMs)
In this paper, Large Language Models are only used ceneral-purposs auxiliary tools, primarily for document-leved auxiliary tasks such as grammar checking and expression refinement. LLMs did not participate in the conceptualization, method derivation, experimental design of this research, nor did they contribute to any core writing content.
H MORE PERFORMANCE VISUALIZATION
Here, we present more visual comparison results between Full Precision model and W4A4 QuantVGGT in Fig. 9 and Fig. 10.
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VGGT (FP16)
QuantVGGT (W4A4)
Figure 9: Visual comparison results.
VGGT (FP16)
QuantVGGT (W4A4)
Figure 10: Visual comparison results.
To further demonstrate the effectiveness of quantized model, provide additional comparison with RTN and QuaRot (Ashkboos et al., 2024) in Fig. 11, Fig. 12, and Fig. 13.
Ground Truth
V66T
RTN
QuaRot
QuantVGGT
Figure 11: Visual comparison results with more methods.
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Ground Truth
VGGT
RTN
QuaRot
QuantVGGT
Figure 12: Visual comparison results with more methods.
Ground Truth
V6GT
RTN
QuaRot
QuantVGGT
Figure 13: Visual comparison results with more methods.
22


Reviews:
[{"review_id": "YK3xGDHxtA", "reviewer": "Reviewer_KMES", "summary": "The paper tackles PTQ of billion‑parameter VGGT, a recent 3D reconstruction backbone. The authors diagnose two obstacles for VGGT PTQ: (i) heavy‑tailed activations induced by data‑independent special tokens (camera & register tokens), and (ii) unstable calibration due to the multi‑view nature of 3D inputs. They propose Dual‑Smoothed Fine‑Grained Quantization (DSFQ) and Noise‑Filtered Diverse Sampling (NFDS) to address the issues.", "questions": "Have you tried QuantVGGT on models without VGGT’s special tokens, eg. Fast3R? Does NFDS remain effective?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper provides convincing evidence that VGGT’s data‑independent registration tokens produce heavy‑tailed, high‑variance channels that break naïve PTQ; the visualizations in Fig. 3 and Fig. 7 make this concrete. \n2. DSFQ is well‑motivated: Hadamard rotation preserves matmul but spreads outliers, and the subsequent channel scale is computed after rotation, avoiding pre‑scale instability. The choice of token‑wise activation is sensible for transformer matmuls and validated in Table 5.\n3. Strong empirical results at low bit‑widths. W4A4 QuantVGGT clearly surpassing generic baselines.", "weaknesses": "1. The baseline set is strong (GPTQ, SmoothQuant, QuaRot, DopQ‑ViT), but some competitive activation‑aware baselines (e.g., AWQ‑style or ViT‑specific PTQ variants) are not included; also unclear if all baselines received equal calibration size/selection tailored to 3D sequences.\n2. External validity beyond VGGT. Claims focus on VGGT‑1B and VGGT is a great model. However, it is unclear whether the observations and designs can translate to other 3D backbones.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "4dJGKMcxRk", "reviewer": "Reviewer_48HL", "summary": "This paper presents QuantVGGT, the first post-training quantization (PTQ) framework specifically designed for Visual Geometry Grounded Transformers (VGGTs) they propose:\nDual-Smoothed Fine-Grained Quantization (DSFQ) — applying a Hadamard pre-rotation and channel-wise post-smoothing to reduce outlier sensitivity and inter-channel variance.\nNoise-Filtered Diverse Sampling (NFDS) — filtering outliers using deep-layer activation statistics and building frame-aware diverse calibration clusters for robust calibration.", "questions": "Do the authors plan to further validate the robustness of their method on more diverse 3D datasets (e.g., ScanNet, DL3DV, or Tanks and Temples)?\n\n\nHow does QuantVGGT perform under mixed precision or asymmetric quantization setups?\n\nHow sensitive is QuantVGGT to the choice of Hadamard matrix and smoothing coefficient α?\nWould small perturbations in these hyperparameters impact stability?\n\nDoes the method generalize to other architectures such as DUSt3R or diffusion-based 3D transformers?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "This is the first quantization framework tailored for billion-scale 3D transformers, a meaningful step for deploying 3D vision foundation models efficiently.\n\nAblation studies clearly isolate contributions from DSFQ and NFDS.\n\nQuantizing VGGT can significantly lower compute/memory costs for 3D perception and reconstruction in real-world systems", "weaknesses": "Only two datasets (Co3Dv2, DTU) are used. Broader testing on outdoor scenes could strengthen claims of generality.\n\nThe DSFQ pipeline introduces additional preprocessing (Hadamard transform, smoothing, and clustering). The cost and integration details for deployment (e.g., on mobile devices) are not deeply analyzed.\n\nThe paper only focuses on PTQ, so it’s unclear how much performance could be recovered with small-scale fine-tuning.\n\nTheorem 3.2 is theoretically elegant but its real contribution to calibration robustness could be better demonstrated through comparative sampling visualizations or empirical sensitivity analysis.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "hAcXFy97oV", "reviewer": "Reviewer_o5ar", "summary": "This paper introduces QuantVGGT, the first post-training quantization framework specifically designed for billion-parameter Visual Geometry Grounded Transformers (VGGTs), tackling two unique challenges in 3D reconstruction: (1) Dual-Smoothed Fine-Grained Quantization (DSFQ) employs a pre-global Hadamard rotation to disperse heavy-tailed activations from data-independent tokens, followed by post-local channel smoothing to stabilize variance without runtime overhead; and (2) Noise-Filtered Diverse Sampling (NFDS) filters outliers using deep-layer statistics and constructs calibration clusters based on VGGT’s intrinsic frame-relative geometric bias, ensuring representative sampling. The approach achieves state-of-the-art results under 4-bit quantization, preserving over 98% of full-precision accuracy while delivering 2.5× speedup and 3.7× memory compression—enabling practical deployment of large 3D vision models in resource-constrained environments.", "questions": "Q1: How does performance degrade as the calibration set size is significantly reduced (e.g., to 20, 10, or 5 samples)?\n\nQ2: In the extreme low-data regime (e.g., <10 samples), does your NFDS method maintain superior stability (lower variance) compared to random sampling?\n\nQ3: How specific are the identified quantization challenges to VGGT? Could the core principles of DSFQ and NFDS be applied to other 3D vision transformers like DUSt3R or MASt3R?\n\nQ4: Could you clarify what \"random Hadamard matrix\" means? How was it generated, and is the model's performance sensitive to the specific matrix chosen?\n\nQ5: Is the remaining performance gap at W4A4 a fundamental limitation of PTQ for this model? How would it compare against a minimally fine-tuned QAT baseline (e.g., after 1 epoch)?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "S1: Novel and Insightful Problem Formulation. The paper's core originality lies not in inventing a new algorithm, but in being the first to identify and formalize the unique quantization challenges specific to 3D vision transformers—namely, the instability caused by data-independent special tokens and multi-view calibration. This re-framing of the problem is a significant contribution in itself.\n\nS2: High-Quality and Tailored Technical Solutions. The proposed methods (DSFQ and NFDS) are technically robust and specifically tailored to the identified problems. In particular, NFDS cleverly leverages the model's own geometric inductive bias for sampling, demonstrating a deep understanding of the architecture that goes beyond generic quantization techniques.\n\nS3: High Practical Significance and Impact. The work's significance is substantial, as it bridges the gap between powerful, large-scale 3D models and real-world deployment. Achieving 98% accuracy at 4-bit with significant memory and latency reduction makes these models viable for edge devices, unlocking new possibilities in robotics, AR/VR, and other resource-constrained applications.\n\nS4: Excellent Clarity and Presentation. The paper is exceptionally clear and well-written. The use of informative figures to visualize abstract concepts like activation distributions, combined with intuitive and rigorous explanations, makes the complex contributions accessible and highly convincing.", "weaknesses": "W1: Lack of Real-World Hardware Benchmarks. The reported latency and memory gains are theoretical. The paper lacks empirical validation on actual hardware using standard inference engines (e.g., TensorRT, ONNX Runtime), making it difficult to confirm if the claimed speedups translate to real-world deployment.\n\nW2: Unclear Sensitivity to Calibration Data Size. The robustness of the method to the number of calibration samples is not explored. An ablation study on performance with significantly fewer samples (e.g., 10 or 20) is missing, which is critical for understanding its practicality in data-scarce scenarios.\n\nW3: Limited Generalization to Other Architectures. The evaluation is limited exclusively to the VGGT architecture. It remains unclear if the proposed solutions are generalizable to other 3D vision transformers (e.g., DUSt3R, MASt3R), which limits the broader impact of the work.\n\nW4: Missing Comparison to Quantization-Aware Training (QAT). The paper does not include a comparison to QAT. A QAT baseline, even with minimal fine-tuning, would provide crucial context on the absolute performance ceiling and help better evaluate the effectiveness of this PTQ-only approach.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "cbhp0SE8Ck", "reviewer": "Reviewer_7NMK", "summary": "This submission presents QuantVGGT, the first Post-Training Quantization (PTQ) framework for Visual Geometry Grounded Transformer (VGGT), a state-of-the-art model for learning-based 3D reconstruction. The work addresses two critical challenges in VGGT quantization: (1) heavy-tailed activation distributions induced by data-independent camera and register tokens, and (2) unrepresentative calibration dataset building due to the multi-view complexity of 3D data.\nFor challenge 1, the authors propose Dual-Smoothed Fine-Grained Quantization (DSFQ), which consists of a pre-global Hadamard rotation for dispersing outliers and smoothing heavy tails, a post-local channel scaling for normalizing inter-channel variance. Furthermore, the authors adopt token-wise quantization for activation and outer-dimension-wise quantization for weight, forming fine-grained quantization, which leads to minor quantization error.\nFor challenge 2, the authors propose Noise-Filtered Diverse Sampling (NFDS), which filters noisy outliers using deep-layer activation statistics and constructs frame-aware calibration clusters concerning VGGT’s inductive bias of relationships between first-frame and subsequent-frames.\nComprehensive experiments on Co3Dv2 for camera pose estimation and DTU for point map estimation demonstrate that, QuantVGGT outperforms existing quantization methods across various bit-widths both in theoretical validation and real-hardware deployment.", "questions": "1. For frame-aware clustering in NFDS, the correlation vector c^i measures similarity between the first frame and subsequent frames. How does this strategy perform on real-world 3D sequences where the first frame is an outlier (e.g., occluded or low-light)?\n\n2. Why does the QuantVGGT in W8A8 outperform the FP16 baseline in Camera Pose Estimation on Co3Dv2? Please analyze the phenomenon and clarify the potential causes. Moreover, provide a reasonable explanation for the marginal performance difference between QuantVGGT and QuaRot under W8A8 setting.\n\n3. Is there a possibility of deploying QAT or extremely low-bit PTQ in VGGT? How should the quantization strategies be adapted or specially designed for these scenarios respectively?\n\n4. Have you ever studied whether other 3D reconstruction models (e.g., DUSt3R, MASt3R) also have similar phenomenon of heavy-tailed activation distribution and unstable calibration? Would DSFQ and NFDS perform well on them?\n\n5. Presentation and Visualization Suggestions: \n(1) Supplement brief explanations of variables R_j^*, V_j^*, and V^* directly in Theorem 3.2, and add a hyperlink to Appendix A immediately for quick reference to related details.\n(2) Consider enlarging the size of Fig. 4 (a) and supplementing the specific attributes (labels or features) corresponding to each cluster in Fig. 4 (b)(c)(d) in Appendix E to improve readability.\n(3) Include a concise introduction to \"global robust moments\" before Eq. 9.\n(4) Give an explicit definition of \"static quantization\" and \"dynamic quantization\" and clarify whether \"tensor-wise\" and \"token-wise\" refer to activation quantization specifically.\n(5) Merge Table 6 in Appendix D into Table 3 of Sec 4.3 to avoid redundant presentation.\n(6) Provide detailed hardware platform information together with absolute latency and optimize the presentation format of Fig. 6.\n(7) Consider consolidating foundational knowledge of rotation-based quantization (e.g., Hadamard transform) and migration-factor-based quantization (e.g., SmoothQuant) into Sec 3.1 (Preliminary) for better logical coherence.\n(8) Add optional ablation studies for hyperparameters, including the filtering threshold T (or p), the cluster number K and the calibration samples scale N.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. Originality: Target model quantization on VGGT, a new SOTA 3D reconstruction model, analyzing concrete experimental phenomena, heavy-tailed activation distribution and unrepresentative calibration dataset, and proposing corresponding observation-driven solutions, DSFQ and NFDS.\n\n2. Validation: Detailed mathematical theoretical derivations and justifications for technical innovations. Comprehensive experiments with additional ablation studies across tasks, quantization bit-widths, comparison methods, and proposed components.\n\n3. Clarity: Hierarchical narrative logic, clear writing, well-organized sections, and informative visualizations make complex concepts (e.g., Hadamard rotation and frame-aware sampling) accessible to readers from both 3D vision and quantization backgrounds.\n\n4. Significance: Bridge the gap between large-scale 3D reconstruction model performance and deployment efficiency, with results that are both scientifically influential and practically useful, which advances model quantization in 3D field and enables edge 3D reconstruction.", "weaknesses": "1. Insufficient Related Works: Not involve other works on VGGT acceleration and optimization, such as FastVGGT using token compression and FasterVGGT using sparse attention, let alone comparison with these methods in Sec 4 to highlight the efficiency of QuantVGGT.\n\n2. Rough Analysis of Special Tokens: The paper mentions that data-independent special tokens cause heavy tails, but does not explicitly and concretely state the respective influences of camera tokens and register tokens.\n\n3. Experiment Generalization:\n(1) Lack of experiments on the widely adopted W4A8 quantization configuration to enable more comprehensive comparisons with existing methods.\n(2) Lack of experiments on more tasks (e.g., Multi-view Depth Estimation on DTU, Image Matching on ScanNet-1500) and datasets (e.g., Camera Pose Estimation on RealEstate10K, Point Map Estimation on ETH3D) compared with the original VGGT paper.\n\n4. Experiment Credibility:\n(1) In Sec 3, the authors claim that VGGT has an inductive bias for modeling relationships between the first frame and subsequent frames. From the theoretical perspective, this phenomenon perhaps originates from the unique design of VGGT’s two distinct sets of special tokens. However, the paper barely supply sufficient experimental evidence to reflect it.\n(2) Activation Distribution only in two adjacent blocks (frame/global block 7/8) in Appendix D fail to confirm the claimed ubiquity of the heavy-tailed phenomenon across different layers.\n(3) Lack of more subjective visualization results in Appendix H in across more scenarios, including comparisons between the FP16 baseline, QuantVGGT, and other quantization methods under various quantization settings.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "PYR35MZkPC", "reviewer": "Reviewer_9UAK", "summary": "This paper proposed QuantVGGT, the first comprehensive post-training quantization framework designed for visual geometry grounded transformer. QuantVGGT consists of two components: Dual-Smoothed Fine-Grained Quantization architecture for smoothing the distribution for better quantization performance from both global and local perspective and Noise-Filtered Diverse Sampling strategy for constructing information-maximized calibration dataset. Extensive experiments on multiple tasks compared with different strong baseline methods shown that QuantVGGT greatly outperforms the compared methods and achieves SOTA performance across different bit-width and tasks.", "questions": "1. The symbol writing on line 192 seems to be incorrect. And for the compared method in line 377, I believe it should be spelled QuaRot.\n\n2. Providing acceleration effects at different sequence lengths will further enhance its practicality in different scenarios.\n\n3. Adding different comparison methods reconstruction visualization will better demonstrate the visual effectiveness of the proposed methods.", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The motivation of QuantVGGT is clear and quite meaningful, as a systematic quantization research on large-scale 3D foundational models in is heavily unexplored and crucial.\n\n2. The proposed method is supported by extensive empirical analysis and theoretical theorems, and appears to effectively solve the corresponding problems.\n\n3. The paper writing is very clear and accompanied by key illustration figures and method flowcharts, which are easy to understand and implement.\n\n4. The paper conducted high bit (8-bit) and low bit (4-bit) experiments on multiple task datasets, significantly surpassing strong baseline methods from different fields, demonstrating the effectiveness and generalization of QuantVGGT.\n\n5. QuantVGGT reported the true efficiency in hardware, calibration consumption, various detailed ablation experiments, and extensive experimental analysis. This fully demonstrates the effectiveness of its method and the effectiveness of acceleration in real-world scenarios.", "weaknesses": "1. The symbol writing on line 192 seems to be incorrect. And for the compared method in line 377, I believe it should be spelled QuaRot.\n\n2. Providing acceleration effects at different sequence lengths will further enhance its practicality in different scenarios.\n\n3. Adding different comparison methods reconstruction visualization will better demonstrate the visual effectiveness of the proposed methods.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)