DispViT: Direct Stereo Disparity Regression with a Single-Stream Vision Transformer
Authors: Tongfan Guan
Jiaxin Guo
Tianyu Huang
Jinhu Dong
Chen Wang
Yun-Hui Liu
Venue: ICLR 2026 Poster (2026)
Keywords: stereo disparity estimation
vision transformer
positional encoding
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: DispViT replaces matching-centric stereo with direct disparity regression in a single-stream ViT, achieving SOTA accuracy, improved robustness, and higher efficiency

Abstract: Deep stereo disparity estimation has long been dominated by a \textbf{matching-centric paradigm}, built on constructing cost volumes and iteratively refining local correspondences.
Despite its success, this paradigm exhibits an intrinsic vulnerability: visual ambiguities from occlusion or non-Lambertian surfaces invevitably induce errorneous matches that refinement cannot recover.
This paper introduces \textbf{DispViT}, a new architecture that establishes a \textbf{regression-centric paradigm}. 
Instead of explicit matching, DispViT directly regresses disparity from tokenized binocular representations using a single-stream Vision Transformer.
This is enabled by a set of lightweight yet critical designs, such as a probability-based disparity parameterization for stable training and an asymmetrically initialized stereo tokenizer for effective view distinction.
To better align the two views during stereo tokenization, we introduce a novel shift-embedding mechanism that encodes different disparity shifts into channel groups, preserving geometric cues even under large view displacements.
A lightweight refinement module then sharpens the regressed disparity map for fine-grained accuracy.
By prioritizing holistic regression over explicit matching, DispViT streamlines the stereo pipeline while improving robustness and efficiency.
Experiments on standard benchmarks show that our approach achieves state-of-the-art accuracy, with strong resilience to matching ambiguities and wide disparity ranges.
Code will be released.

Published as a conference paper at ICLR 2026
DISPVIT: DIRECT STEREO DISPARITY REGRESSION WITH A SINGLE-STREAM VISION TRANSFORMER
Tongfan Guan' Jiaxin Guo', Tianyu Huang', Jinhu Dong', Chen Wang", Yun-Hui Liu The Chinese University of Hong Kong University at Buffalo *Corresponding Author
ABSTRACT
Deep stereo disparity estimation has long been dominated by a matching-centric paradigm, which constructs cost volumes and iteratively refines local correspon- dences. Despite its success, this formulation exhibits an intrinsic vulnerability: vi- sual ambíguities from occlusion non-Lambertian surfaces inevitably induce er- roneous matches that local refinement cannot fully recover. This paper introduces ViT, new architecture that establishes regression-centric paradigm. In- stead of explicit matching, ViT directly regresses disparity from tokenized binocular representations using single-stream Vision Transformer. This is en- abled by set of lightweight yet critical designs, such as a probability-base dis- parity parameterization for stable training and an asymmetrically initialized stereo tokenizer for effective view distinction. To better align the two views during stereo tokenization, we introduce novel shift-embedding mechanism that encodes mul- tiple disparity shifts into channel groups, preserving geometric cues even under large view displacements.
lightweight refinement module then sharpens the re- gressed disparity map for fine- grained accuracy. By prioritizing holistic regression over explicit matching, ViT streamlines the stereo pipeline while improving robustness and efficiency. Extensive experiments standard benchmarks demon- strate sate--of-the-ar accuracy, along with strong resilience to matching ambigui- ties and wide disparity ranges.
INTRODUCTION
Stereo disparity estimation is one of the core challenges in computer vision, with applications in autonomous driving (Geiger et al. 2013), augmented reality (Kim et al. 2018), and robotic manipu- lation (Fang et al. 2023). The objective is to compute the horizontal displacement of pixels between two rectified images from a stereo camera rig. A dominant paradigm for stereo disparity estimation has been matching-centric, which explicitly establishes pixel-Ievel correspondence between the left and right views. This perspective has driven the prevalence of pipelines (Zbontar & LeCun 2016 Kendall et al.] 2017: [ipson et al., 2022 Li et al.) (022)) built around cost volumes and iterative refinement. While effective, the matching-centric paradigm exhibits an intrinsic limitation: match- ing is inherently ill-posed in the presence of visual ambiguities such as transparency, occlusion, or repeated patterns. Moreover, unreliable matches are often difficult to recover via subsequent local refinemnnts leaving the pipeline brittle in cases where robustness is critical. This motivates to re- think stereo disparity estimation from different perspective one that bypasses explicit matching.
Currently, Vision Transformers (ViTs) have demonstrated remarkable capabilities in geometry re- gression tasks like monocular depth estimation (Yang et al. 2024 Piccinelli et al. 2024) and feed- forward 3D reconstruction (Wang et al. 2024a 2025). However, the use of VIT's in stereo networks has been largely confined to view feature extractors (Wen et al. 2025 Liu et al., 2024) within conventional matching pipelines, leaving their potential for direct stereo disparity regression largely unexplored. In this work, we advocate a regression-cemie centrrcc perspective: rather than building increas- ingly elaborate cost volumes and refinement mechanisms, we harness the global reasoning capacity of ViT (Dosovitskiy et al., (020) to perform direct disparity regression through holistic analysis of visual context and binocular cues. This holistic regression yields a strong initial estimate, which complement with a lightweight refinement module for fine-grained accuracy. By rethinking stereo
Code available at: https: http:p://ggthub.coom www.tit.......eee.oeoeooeo wwg.o//uunannnoooo panpuann/iiii spVi'

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disparity estimation around regression rather than matching, our approach circumvents the core vul- nerability of matching pipelines: their susceptibilityy to visual ambiguities.
This paradigm shift raises a key design question: how should binocular images be tokenized for VIT to enable direct regression? Conventional matching-centii approaches encode the two views separately (Li et al. 2021 Weinzaepfel et al. 2023) for establishing matching. Departing from this dual-stream manner, our regression- first philosophy pursues a single-stream formulation. The pioneering regression-centric work of DispNetS (Mayer et al., 2016) simply concatenated stereo pairs along the channel dimension for a CNN to regress disparity. While conceptually elegant, this method was fundamentally limited by the localized receptive field of convolution, which hindered effective reasoning over large disparities or complex global contexts, ultimately constraining gen- eralization. The global attention mechanism of ViTs offers an effective remedy, enabling holistic reasoning across both views without the locality bottleneck. However, directly concatenating the two views poses its own challenge: significant pre-attention misalignment at the token level caused by large disparities, which corrupts the ViT's input and impedes its ability to infer binocular geome- try. This motivates a new stereo tokenization design that is expected to mitigate early misalignment and allow single-strean ViT to operate effectively.
To this end, we propose a shift- shift-embedding stereo tokenizer that mitigates input-level misalignment by horizontally shifting the right view with a set of predefined offsets. Each shifted variant is in- dependently tokenized into a separate channel group and then blended with the left-view tokens, allowing each spatial token encode spectrum of potential alignments and easing reasoning over large disparities. Complementing this, we introduce a simple yet effective asymmetric initializa- tion of the patchification convolution, which distinguishes the left and right views from the earliest stage of training and prevents early degeneracy. To further embed binocular geometry priors into holistic reasoning, we extend Rotary Position Embeddings (RoPE) (Su et al., 2024) disparity- aware formulation (DA-RoPE), enabling aggregated features to remain geometry consittent even at large displacements. Together, these lightweight yet critical components form the foundation of Dis- PViT, a single- stglammtteam Vision Transformer that successfully bypasses explicit matching and directly regresses disparity from a holistically reasoned binocular representation, as depicted in Figure [
Our ViT, pretrained on large corpus of data, demonstrates strong robustness matching am- biguities (see 4). When complemented with a lightweight refinement module for fine-grained details, it achieves state-of-tt of-e--of-the-ar performance while maintaining efficiency. In summary, we intro- duce Disp ViT, the first single-stream ViT framework that bypasses explicit matching and directly regresses stereo disparity from tokenized binocular representations. At its core is single-stream ViT backbone, equipped with shift-embedding stereo tokenizer, probability-ben -based parameterization of disparity, asymmetric initialization, and Disparity-Aware RoPE (DA-RoPE). This regression-fire formulation provides a strong disparity initialization that, together with lightweight refinement, es- tablishes robust and efficient alternative to long-standing matching-centric pipelines.
2 RELATED WORK
Deep Stereo Matching. The foundation of modern deep stereo matching is built upon the pipeline that first extracts discriminative features from a binocular pair and then establishes matching through cost volume or cross- cross-view attention. Seminal works such as GC-Net (Kendall et al. (017)) and PSM- Net (Chang & Chen 2018) pioneered the 3D cost volume architecture: using 2D CNNs for feature extraction, constructing 3D volume, and processing it with 3D convolutions for cost aggregation. Subsequent research has extensively refined this framework by incorporating richer contextual in-
formation (Xu et al. 2022 Shen al. 2022; 202T) and developing more powerful aggregation networks (Zhang et al.) 2019 Guan et al. 2024). RAFT-Sterco (Lipson et al. (021)) fundamentally
shifted the matching paradigm by replacing explicit cost volume processing with recurrent decoder that queries pre-computed, multi-scale 4D correlation space for iterative disparity refinement. This paradigm evolved through innovations like IGEV's geometric encoding (Xu et al. 2023a), the fre- quency decomposition of DNLR (Zhao et al. 2023) and Selective-Steree (Wang et al. 2024b), and Mocha-Stere 's motif- based attention (Chen et al., 2024), collectively improving accuracy, robust- ness and generalization. In contrast with iterative local refinement, another line of research (Li et al.) 2021 Xu et al. 2023b Weinzacptel et al. 2023 Min et al., (202)) employs cross-view attention be- tween separately encoded view features perform global matching.
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Stereo Tokenization
R(p
DA- RoPE
Attention
ParchEmbed
VIT
R(pi)
RI Pd
Encoder
R(ps)
Left Image
Prob. Param,
Shift Embedding
PatchEmbod
Shifted Channei
DPT Head
Right Image
Disparity
Overview of ViS. We introduce regression-centre paradigm for stereo disparity estimation using a simple single-stream ViT. The effectiveness of this simple architecture is enabled by lightweight yet critical designs, such as probability- based parameterization disparity and stereo tokenizer exemplified here, among other critical components explored in the text.
Figure 1:
Recognizing the inherent anbiguities of matching, a growing line of research leverages monocular depth estimation to bootstrap stereo. Recent approaches such Monster (Cheng al. 2025) and DEFOM-Steren (Jiang et al., 2025) initialize RAFT-style iterative refinement with a scale-aligned monocular disparity map. This initialization, inherently free from matching ambiguities, provides strong prior that markedly enhances both the accuracy and robustness of stereo disparity estimation. The concurrent BridgeDepth (Guan al. 2025) unifies monocular and stereo reasoning through iterative bidirectional alignment of latent representations, efficiently synthesizing stereo precision with monocular robustness. All these advances harmess the core advantage of monocular disparity regression: immune to matching ambiguities. The success of these hybrid approaches inspires our pivotal departure- to directly regress disparity from the binocular input without explicit matching.
VITs for Correspondence. ViTs have demonstrated strong capacity in modeling visual corre- spondence, typically following an encoder-aggregatoo architecture. Feed-forward 3D reconstruc- tion methods such as MAS13R (Leroy et al. (0224) and VGGT (Wang et al. 2025) ground image matching in 3D reconstruction, producing structure-aware dense features to establish cross-vics view correspondences. Furthermore, C2ocoV2 (Weinzaepfel et al. 2023) and UFM (Zhang et al.) 2025)
directly predict correspondence fields with DPT (Ranftl et al. [222]] head after feature aggregation using cross-attention alternate-attentioon. In stereo matching. FoundationStereo (Wen al.) 2025)
employs a ViT as the feature extractor, complemented by CNN to fuse global context with fine details. Despite architectural variations, these approaches are inherently matching-centric focusing on aligning features across images. In contrast, Disp ViT departs from the encoder-aggregator design and introduces a single-stream ViT that directly regresses disparity from binocular input, recasting stereo matching as regression rather than correspondence search.
3 METHOD
Given rectified stereo pair (I,, E IR xx goal is predict the disparity map of the left view D E HR this end, we propose transformer-bet based architecture that directly regresses the disparity map from unified token representation of the two views using single-stream ViT:
D, DPT (n),
where T tokenizes the binocular input into single sequence and + denotes ViT enhanced with novel disparity-aware Rotary Positional Embeddings (RoPE). A DPT (Ranfil et al., (021) head fuses the transformer's multi-scale features to regress an initial disparity map D,, which subsequently sharpened by a lightweight refinement module to produce the final prediction D.
3.1 STEREO TOKENIZATION
Stereo tokenization the crucial first step for enabling direct disparity regression within a single- stream VIT. It patchifies the left and right views and blends them into single sequence. To inherit
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the power of pretrained models, the PatchEmbed module of DINOv2 (Oquab et al. 2023) is
adapted to handle binocular input. This standard tokenization layer, denoted as E, is essentially a strided 2D convolution, mapping 3-channel RGB to patchified nigh-dimensional embeddings. A straightforward extension would be to concatenate the two views along the channel dimension, du-
plicate the convolution weights, and scale them by half, in Marigold (Ke et al. (024) when adapting Stable Diffussio to monocular depth estimation.
However, we found asymmetric initialization yields substantially superior performance in practice. Concretely, we initialize the new convolution kernel by concatenating the original pretrained weights with a zero tensor of identical shape instead of duplicating and halving them. We hypothesize this "half-zero" initialization provides a critical inductive bias: the pretrained branch processes the left view as a clear, stable reference, while the zero-initialized branch is compelled to learn specialized features that complement the reference from the right view. This asymmetry encourages the model to distinguish the two views from the first layer, property that symmetric initialization lacks.
Furthermore, large disparities introduce spatial misalignment between the two views. Direct channel concatenation this case mixes features from unrelated image regions, leading to incoherent token representations. To mitigate this, we design shift-embedding tokenizer that encodes multiple alignment hypotheses within each token. Let {sk]k= be a set of predefined horizontal shift offsets. For each offset sg, the right image IR is shifted by sy and passed through a specific convolution & producing channel groups
(k) & (Shift(IRR sk)), k k= K.
(2)
These groups are concatenated along the channel dimension to form the right-view embedding TR-
Concat ((TH() }=))), Meanwhile, the left view In is tokenized by the pretrained PatchEmbed 3
The asymmetric initialization is still employed, i.e.. & retains the pretrained weights while { are initialized with zeros. Finally, the stereo tokens are blended pixelwise by summation, T Tr. TT. yielding unified token sequence in which each spatial token embeds spectrum of potential disparities. This design preserves disparity structure at the input level and facilitates ViT's reasoning over large displacements. Compared direct channel concatenation, our shibt-embedding tokenizer incurs negligible overhead since Tn can be implemented using an optimized groupwise convolution, while outperforming with an appreciated margin.
Although shift-embedding introduces multiple horizontally shifted variants of the right view, it nei- ther constructs nor approximates cost volume. Unlike cost volumes, which explicitly compute pairwise feature similarities across disparity hypotheses, shift-embedding performs no correlation or cost aggregation, but instead encodes coarse alignment cues into channel groups during tokeniza- tion. Disparity is then directly regressed from tokenized stereo input by a single-stream ViT, making shift-embedding an alignment prior rather than an explicit matching mechanism.
3.2 SINGLE-STREAM VISION TRANSFORMER
At the core of architecture lies single-stream Vision Transformer (ViT), which unifies feature extraction and correspondence reasoning within a single Transformer backbone, thereby bypassing the need for explicit matching modules. We build upon a pretrained DINOv2 ViT backbone, capital- izing on its robust visual representations while ensuring compatibility with our stereo tokenization scheme. The transformed multi-scal. features are then consumed by a DPT head to predict disparity.
To provide the ViT with spatial awareness, DINOv2 adds learnable absolute positional embeddings (APE) to each token. However, we found APE ill-suited for disparity regression. We hypothesize the issue lies in its lack of translational equivariance: disparity is inherently relative offset, yet APE encodes only absolute locations without an effective mechanism to capture relative displacements. Unlike APE, Rotary Positional Embeddings (RoPE) (Su et al., 2024) encode positions by rotating queries and keys such that attention depends on relative offsets rather than absolute coordinates. This inductive bias aligns naturally with stereo geometry, where disparity manifests as horizontal trans- lation. Empirically, we observe that substituting APE with RoPE brings substantial performance gains, underscoring the necessity of modeling relative geometry in disparity regression.
While RoPE ensures attention weights are translationally equivariant, leaves the value vectors agnostic relative position. But, for disparity estimation, the semantic meaning of feature is
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FUE
Figure 2: Zero-shot generalization real-world datasets. Qualitative comparison with Selective- IGEV, NMRF, and DEFOM-Stere across four datasets. Our Disp (last column) exhibits supe- rior robustness matching ambiguities, including low-texture regions ("black holes") and complex surface materials like reflections and transparency. Best viewed in color and zoomed in.
intrinsically tied to its position relative to the viewer. Motivated by this intuition, we introduce Disparity-Aware RoPE (DA-RoPE), which conditions value encoding on relative position. Con- cretely, each value vj is rotated by its position R(P;), aggregated with the attention weights, and counter-rotated by the query position (-p)). The resulting representation
2; R(-pp))) Eay =
=E ais aa R(p, - PP)V,
(3)
equivalent rotating each v, by the relative position Pj - P: before aggregation. Intuitively, DA-RoPE re-expresses features in the query's local reference frame before aggregation, ensuring that both attention weights and aggregated features are consistently disparity- aware. This design embeds translational equivariance directly into the value pathway and equips the ViT with stronger inductive bias for robust disparity estimation, especially under large disparities.
Prediction head, We adopt DPT head to fuse multi-scale features from the ViT backbone for dis- parity estimation. A key design choice lies in the parameterization of disparity prediction. Instead of regressing disparity values directly, discretize the disparity range into uniformly spaced bins and let the head output a probability distribution over these bins (inspired by Zholus et al. (2025)) The final disparity estimate is computed as the expectation over the distribution within local window around the peak probability. This probabilistic formulation provides two advantages: it naturally reflects the bounded disparity range with well-structured output space, and it allows the model to capture uncertainty in ambiguous regions rather than collapsing to a scalar value. Experiments suggest this probability parameterization is one of the most important components in our archi- tecture. The network is supervised with combined loss function that includes cross-entropy loss for the discrete distribution P and an L1 loss on the continuous estimate Do to ensure accuracy, i.e.,
Gragreza CE(P, bilinear(D')) Ai D*),
where bilinear(D')) denotes the bilinear assignment ground-truth disparity to discrete bins.
3.3 REFINEMENT
While the single-stream VIT generates strong and robust disparity estimate, it inevitably misses fine-grained details, as no explicit two-view comparison performed within the backbone. To re- cover these details, we introduce lightweight refinement module applied after the direct regression. This module revisits the stereo pair and focuses on local correspondence cues, enabling sharper ob- ject boundaries and better reconstruction of thin structures. By design, it complements the VIT's global reasoning with precise local matching, yielding a complete and accurate disparity estimate.
To maintain efficiency, our refinement avoids reconstructing cost volume. Instead, adopt the refinement module NMRF (Guan et al. 2024), which anchors local matching on a single-pass geometry warping guided by the initial disparity estimate, in contrast to the iterative cost-volume indexing of RAFT-style refinement. Specifically, the predicted disparity is used to warp the right image features toward the left view, producing aligned representations that highlight local inconsis-
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tencies. A lightweight Swin Transformer (Liu al. 2021) then integrates the warping feature fields with the initial disparity prediction to correct fine details.
Decoupled training. We adopt a two-stage training scheme to preserve modularity and flexibility. First, our single-stream ViT is trained independently using the loss presented in Equation yielding robust direct disparity regressor. Subsequently, we train the refinement model following the NMRF protocol, with the ViT regressor kept frozen. This decoupled strategy ensures that the ViT regressor can serve standalone model, directly deployable in applications where efficiency is paramount, or seamlessly integrated with external refinement modules to recover fine-grained details.
3.4 DISCUSSION
A central contribution of this work is establishing a strong baseline for direct disparity regression, which has traditionally been considered ill-posed due to the lack of explicit matching mechanisms. This capability is unlocked by several key designs. In particular: (I) The probability parame- terization of disparity prediction significantly stabilizes training and boosts accuracy compared to scalar value regression; (2) Our shift-embedding stereo tokenizer preserves disparity structure in the blended token representation of two views; (3) The Disparity-Aware RoPE ((DA-RoPE) ex- tension equips ViT attention with the translational equivariance inductive bias essential for disparity matching; (4) Asymmetric initialization prevents early-stage training degeneracy and promotes balanced gradient flow across both views. Collectively, these innovations close the performance gap between the simplistic single-stream regressor and more elaborate matching centric pipelines, while demonstrating strong robustness in ambiguous regions.
EXPERIMENTS
In this section, we describe our implementation details and evaluation protocol. Then we compare pretrained DiS model and the variant enhanced with external refinement module VDispViT+) ViT+) to state-of-the-art methods in terms of accuracy and robustness. Then we ablate the design choices.
Implementation details. Unless otherwise specified, adopt the following implementation. Our model uses a ViT-L backbone initialized with Depth Anything V2 (DAv2) Yang et al. (2024) weights. Tokenizer: The thifd-embedding tokenizer shifts the right image K times, with each shift off- set by 24 pixels, resulting in an embedding dimension of d/8 for each shifted view, where d is the VIT-L channel dimension. Parameterization: Disparity is represented as a probability distribution over 128 bins uniformly discretizing the range [0, 381]. The loss weight a₁ in Equation 4 is set to 0.1. Position encoding: We employ disparity-awart rotary position embeddings (DA-RoPE) with asymmetric frequencies-100 for the vertical direction and 1000 for the horizontal direction-to better capture the geometric priors stereo imagery. Refinement: The refinement module adopts the feature extractor and refinement network of NMRF (Guan al. 2024), while discarding its disparity proposal network and multi-hypothesii inference components to focus solely on refine- ment capability. Training: All models are trained image crops of size 392 x 768. The single- stream regression model is first pretrained on a mixed dataset, consisting of FSD (Wen et al.) 20025) Scene Flow (Mayer al. 2016), TartanAir (Wang al., 2220), CREStereo (Li et al.
2022). In-
Stereo2K [aao et al. (2020). FallingThing (Tremblay et al. 2018 Sintel (Butler et al. 2012). and
Virtual KITTI 2 (Cabon et al. 2020).
Evaluation protocol. We evaluate across five representative datasets to assess performance under both controlled synthetic and challenging real-world conditions. For large-scale synthetic evalua- tion, we use the Scene Flow dataset (Mayer et al., 2016)), which provides over 35,000 training pairs and 4,370 testing pairs at 540 resolution, spanning diverse scenarios from the Flying Things,,,, Driving, and Monkaa subsets. Real-world performance is assessed on the KITTI 2012 (Geiger et al.) 2013) and KITTI 2015 (Menze Geiger 2015) benchmarks, including 194/195 and 200/200 train- ing/testing pairs, respectively, with sparse and inherently noisy LiDAR-basee ground truth from urban driving scenes. To probe domainn generalization, we further conduct uation on the training set of Middlebury V3 (Scharstein et al. 2014), which offers high high-resolution indoor scenes with dense structured-lige light annotations, and ETH3D (Schops et al., 2017). comprising grayscale stereo pairs of indoor and outdoor environments with challenging low-texture regions.
eval-

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Ablation studies. Upper: removal test set. ViT delivers competitive performance, studies, where each component is individually even surpassing the ground-breaking RAFT-Stereo. removed from the baseline to quantify the ne- A lightweight refinement (Disp ViT+) boosts accu- cessity. Lower: addition studies, where com- racy, highlighting the robustness of Disp as a re- ponents are incrementally incorporated into the gression prior. : matching-centerie methods,
Table 1: Quantitative evaluation on Scene Flow Table 2:
baseline model to isolate their contributions.
hybrid methods. t: Benchmarked on RTX 3090.
Model
EPE + BP-1
Time [s]
Method
EPE 4 BP-1 4
Time! [s]
Baseline (VIT-B)
0.89
10.05
0.039
No DAv2 (scratch)
1.81
20.13
0.039
RAFT-Sterco
0.56
6.63
0.40
No DAv2 (DINOv2)
0.92
10.79
0.039
DLNR
0.48
5,39
0.33
No probability
1.07
15.56
0.027
Selective-IGEV
0.44
4.98
0.25
No asymmetric
0.97
11.88
0.039
NMRF
0.45
4.50
0.10
No RoPE (APE)
0,96
13.34
0.042
DEFOM-Stere
0.42
5,10
0.63
addition studies
BridgeDepph
0.37
3.67
0.14
shifld-em (SE)
0.84
9.22
0.040
ViT (Ours)
0.53
5,30
0.092
DA-RoPE (DA)
0.82
8.84
0.042
ViT+ (Ours)
0.34
3.50
0.118
asymmetric freq (AF)
0.76
8.27
0.042
Our evaluation adheres established protocols stereo benchmarking (Mayer et al. 2016 Menze & Geiger 2015 Wen et al.) 2025). We compute three standard metrics over all valid pixels: (1)
End-Point Error (EPE), the mean absolute disparity error pixels; (2) Bad-Pixel Rate (BP-X),
the percentage of pixels whose absolute error exceeds X pixels; and (3) D1, the official KITTI 2015
metric which measures the percentage of pixels with an absolute error greater than pixels and exceeding 5% of the ground-truth disparity.
4.1 COMPARISON TO THE
The core contribution of this work is to introduce a competitive regression-centric paradigm as an alternative to the long-standing dominance of matching-centric approaches. To validate this shift, we compare Disp with leading matching-centric methods and hybrid methods that synthesize monocular regression and stereo matching. Beyond standalone regression, we further demonstrate that complementing Disp with lightweight refinement network achieves state-of-the-an accu- racy, while retaining the robustness and efficiency inherent to the regression-centre paradigm.
Scene Flow. For the Scene Flow benchmark, we follow the convention of restricting evaluation to pixels with ground-truth disparities up to 192 pixels. In this evaluation, our pretrained Dis model is first finetuned all 35,000 training pairs. Subsequently, the refinement network is trained from scratch with the ViT frozen. As shown in Table [ our single-stream regression model achieves performance comparable to leading matching-centre pipelines, e.g., RAFT-Sterco (Lipson) jet al.) 2021). Enhanced with a lightweight refinement module (~225 ms), Disp ViT+ outperforms them with notable margin. Since the refinement module is directly borrowed from NMRF (Guan eet al. 2024), the substantial performance gain (+24%) of Disp over NMRF indicates that the improvement stems from the robustness of Dis as reliable regression prior rather than the refinement architecture itself. This observation resonates with recent trends in hybrid methods like DEFOM-Stere (Tiag al. 2025) and Monster (Cheng al. 2025), and more broadly establishes robust regression priors as new cornerstone for advancing stereo disparity estimation.
Zero-shot evaluation. To assess the generalization of our regression-centric paradigm, we conduct zero-shot evaluation on the training sets of four real-world datasets: KITTI 2012/2015, Middlebury, and ETH3D. We compare our pretrained Disp ViT against strong contemporary baselines, including IGEV,, NMRF, DEFOM-Stereo, and IGEV++ (Xu et al.) 2024). While Disp (VIT-L. backbone) is empowered by large-scale pretraining, which differs from the training regimes of prior methods, we report full quantitative results for transparency and reference. As shown in Table 3 our approach achieves consistently competitive performance across all four benchmarks without dataset-specific fine-tum soee-tunin,, demonstrating strong cross-dataset generalization. Our backbone is ini- tialized from DAv2 and further adapted to stereo disparity estimation through large-scale binocular pretraining, which find essential for robust geometric reasoning under distribution shift. We

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Table 3: Zero-shot generalization evaluation. t: with extra datasets besides SceneFlow.
Trained Table 4: Results on ETH3D leader-
board.
Our method achieves com-
petitive performance on the test set.
KITTI-12
KITTI-15
Middlcbury (Q)
ETH3D (BP-1)
Methods
(D1)
(DI)
(BP-2)
AvgEr (px)
RMSE
Methods
(px)
RAFT-Sterco
4.7
5.5
9.4
3.3
NMRF
4.2
5.1
7.5
3.8
RAFT-Sterco
0.17
0.42
IGEV++
5.9
7.8
4.1
Selective-IEEV
0.15
0.57
DEFOM-Stereo
3.8
5.0
5.7
2.4
IGEV++
0.19
0.74
BridgeDepth
3.6
4.5
4.3
1.3
DEFOM-Stereo
0.11
0.26
Monster
3.6
4.0
5.1
2.0
BridgeDepth
0.11
0,26
Monster
3.0
3.2
2.9
1.2
Monster
0.13
0.47
FoundationStereo
0.13
0.61
DispVIT
4.1
5.5
DispViT+
3.2
3.5
2.4
2.3
Vis+
0.12
0.25
anticipate that further scaling the diversity and realism of stereo pretraining data will continue to improve generalization, particularly for challenging real-world phenomena that remain underrepre- sented in existing synthetic corpora. Qualitative results in Figure 2 further highlight the robustness of ViT. The model performs reliably low-texture regions ("black holes") and on challenging surfaces such as reflections and transparency, where matching-centrie methods like Selective-IGBY and NMRF often fail due to ambiguous correspondences. In contrast, the hybrid method DEFOM- Stereo, despite leveraging DAv2 estimation as initialization, does not fully retain the robustness of its monocular prior, likely due to its iterative refinement remains fundamentally matching- Nonetheless, shown in the last of Figure 2 Dis remains susceptible to extreme visual illusions, such as glass-induced mirror reflections, which are rare in current synthetic datasets. This limitation motivates future work scaling stereo pretraining data distilling the strong prior of monocular depth models to stereo with monocular disparity as affine-invariant supervision to further improve robustness.
Kitti benchmark. For evaluation on the KITTI benchmarks, we freeze the pretrained DispViT model and train only the refinement module from scratch using the combined KITTI 2012 and KITTI 2015 training sets. Unlike synthetic datasets with dense and accurate ground-truth disparity, KITTI annotations are sparse and often noisy, especially near object boundaries. Similar annotation chal- lenges persist in other benchmarks, e.g., ETH3D lacks ground truth for the non-Lambertian surfaces where robustness is most critical (last row Figure 2). This setup presents a particular challenge for our method ViTT)))T+): the frozen regressor cannot adapt to dataset- lightweight refinement to cope with the noisy labels while simultaneously correcting initialization errors from the regressor. To strengthen its capacity for KITTI noise adaptation, we employ a sim- ple strategy, i.e., stacking the refinement network twice. As shown in Table 5 Disp ViT+ achieves superior competitive performance compared to ttate-of-the-art methods. We emphasize that the goal of this evaluation is not to chase leader-board rankings by overfitting to dataset-specific noise, but rather to validate the effectiveness of our regression-centric paradigm real-world scenarios.
noise, leaving the
ETH3D benchmark. We evaluate Disp on the official ETH3D benchmark, a widely adopted testbed for stereo disparity estimation featuring challenging indoor and outdoor scenes with fine structures, textureless regions, and reflective surfaces. As shown in Table our approach achieves competitive performance on this benchmark, demonstrating the effectiveness of direct disparity gression in complex real-world scenarios. Notably, this accuracy is attained with improved inference efficiency compared ffate-ofthh- matching-centee centric pipelines (see Table as our single-stream design eliminates explicit cost-volume construction and costly iterative refinement. Together, these results validate that regression-centte formulation can produce high-fidelity disparity estimation with practical runtime efficiency, offering streamlined yet powerful alternative to the conventional matching-driver driven architectures.
Booster. Finally, we conduct qualitative analysis the test set of Booster dataset (Zama Ramirez et al. 2022) to further examine the behavior of DispViT in complex indoor environments featuring highly reflective and transparent objects. Representative examples in Figure 4 highlights the ro- bustness of Disp under such challenging conditions, while also revealing its limitation cases involving strong mirror-induced visual illusions.
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4.2 ABLATION STUDY
We conduct experiments to examine the impact of each proposed design choice. Unless otherwise mentioned, all experiments use the Scene Flow dataset for training and evaluation, with ViT-B backbone to keep the ablation study more affordable. Since certain components are critical for ViT to reach reasonable level of performance, we first establish baseline configuration that achieves stable and meaningful results. This baseline is defined as a ViT-B model initialized with DepthAnythingg2 (DAv2) weights, combined with probability- -baedd parameterization of disparity (Sec. 3.2), standard RoPE, and asymmetric initialization (Sec. 3.1). From this baseline, perform two complementary analyses: (I) removal studies, where individual components are removed to quantify their necessity, and (2) addition studies, where components are incrementally incorporated into the baseline model to isolate their contributions.
Removal studies. The results of our removal studies summarized in the upper part of Table 2 Removing pretrained weights, ie., training from scratch, causes the most dramatic degradation, with clear signs of overfitting. This underscores the necessity of large-scale pretraining not only for vergence but also as a powerful regularizer, consistent with findings in foundational vision models. We also find that probability parameterization of disparity is essential: by imposing well-structured output space, it stabilizes training and yields substantial performance gain (+17%), though at the cost of ~30% latency overhead. Moreover, both asymmetric initialization of the stereo tokenizer and RoPE prove indispensable, each contributing ~00% EPE reduction by preventing early train- ing collapse and injecting inductive biases aligned with stereo geometry, respectively. Finally, we observe that initializing from DAv2 slightly outperforms DINOv2, suggesting that geometry-aware pretraining provides a stronger prior for stereo disparity estimation.
Addition studies. The results of our addition studies are reported in the lower part of Table 2 We incrementally integrate three proposed designs into the baseline: shift- shift-embedding, disparity-aware RoPE (DA-RoPE)) and asymmetric RoPE frequency (assigning higher frequencies the horizontal axis to better capture horizontal displacements).
Each component yields consistent gains, and together they contribute cumulative +15% improvement. Notably, shift-embedding (SE) and DA-RoPE (DA) specifically enhance performance at large disparities, mit- igating a core weakness of single- stream regression models, illus- trated in Figure However, we also observe a slight trade-off with shift- embedding: while significantly im- proving large-disparity estimation, it marginally degrades accuracy for small disparities (< 32 pixels), likely due to reduced channel capacity per shifted view, which compro- mises fine-grained details necessary for small displacement estimation.
Baseline
SE
1.2
1.0
EPE (pix.)
0.8
0.6
0.5
32
64
96
128
160
192
224
256 288 320 352 384
Disparity Range
Figure 3: End-point error (EPE) across disparity ranges. Shift-embedding (SE) and DA-RoPE (DA) specif- ically improve large-dispariy estimation, while asymmet- ric RoPE frequency (AF) yields consistent gains across all ranges by better encoding horizontal stereo geometry.
These ablation studies provide key insights into the architectural requirements for effective single- stream disparity regression. The results underscore that our lightweight designs act synergistically to meet the core demands of single-stream disparity regression, thereby solidifying the regression- centric paradigm as a powerful alternative matching- pipelines.
CONCLUSION AND FUTURE WORK
We presented VIT, a regression centric architecture for stereo disparity estimation that departs from the dominant matching- based paradigm. Through series of lightweight yet essential archi- tectural adaptations, a single-stream Vision Transformer directly regresses disparity from binocular inputs without explicit cost-volume construction iterative refinement, Despite its conceptual sim-

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Published as a conference paper at ICLR 2026
Table Benchmark results on KITTI 2012/2015 datasets. Metries for KITTI 2012 are the outlier ratio (Out-z) for disparities errors greater than pixels in non-occluded (Noc) and all (All) regions. For KITTI 2015, results are reported using the DI error rate across background (BG) and foreground (FG). (f): Benchmarked on GTX 3090 GPUs.
KITTI 2012
KITTI 2015
Method
Out-2
Out-3
FG
Time
Noc
All
Noc
All
Noc
All
Noc
All
(8)
LEAStereo
1.90
2.39
1.13
1.45
1.29
1 1.40
2.65
2.91
PCWNet
1.69
2.18
1.04
1.37
1.26
1.37
2.93
3.16
ACVNet
1.83
2.35
1.13
1.47
1.26
1.37
2.84
3.07
0.2
RAFT-Stereo
1.92
2.42
1.30
1.66
1.58
3.05
0.38
IGEV-Stereo
1.71
2.17
1.12
1.44
1.27
1.38
2.62
2.67
0.18
Selective-IGEV
1.59
2.05
1.07
1.38
1.22
1.33
2.55
2.61
0.24
NMRF
1.59
2.07
1.01
1.35
1.18
1.28
2.90
3.13
0.09
Mocha-Stereo
1.64
2.07
1.06
1.36
1.24
1.36
2.42
2.43
LoS
1.69
2.12
1.10
1.38
1.29
1.42
2.66
2.81
MonSter
1.36
1.75
0.84
1.09
1.05
1.13
2.76
2.81
0.45
DEFOM-Stereo
1.43
1.79
0.94
1.18
1.25
1.15
2.23
2.24
0.61
IGEV++
1.36
1.74
0.89
1.13
1.07
2.80
2.80
2.80
0.48
BridgeDepthh
1.32
1.65
0.83
1.03
1.05
1.13
2.73
2.62
0.14
Vis+ (Ours)
1.26
1.59
0.82
1.02
1.04
1.12
3.10
3.26
0.15
Left Image
Dis
Figure 4: Qualitative results on the challenging test set of Booster Zama Ramirez et al., 2022) The pretrained DispViT exhibits robustness to matching ambiguities such as transparency, low- texture, and reflections, where conventional matching methods often struggle. However, it remains susceptible to mirroring illusions (last column), a case that monocular models typically handle better.
plicity, Disp achieves competitive benchmark performance and demonstrates strong robustness ambiguous and low-texture regions, highlighting the promise of holistic regression for stereo rea- soning. Looking forward, we plan to further enhance scalability and generalization by expanding stereo pretraining with more diverse and realistic binocular data. We are also interested in distilling strong geometric priors from large-scale monocular depth models into the stereo setting to improve robustness under distribution shift. In particular, addressing challenging mirror-induced visual il- lusions, currently underrepresented in existing stereo corpora, remains important direction for strengthening real-world reliability and practical deployment.
Acknowledgement. This work is supported in part the InnoHK initiative of the Innovation and Technology Commission of the Hong Kong Special Administrative Region Govermment via the Hong Kong Centre for Logistics Roboties, in part by the CUHK T Stone Robotics Institute, in part by and in part by the HK ITF PRP under Grant PRP/038/2FXX.
The authors thank DeepSeek and GPT-5 language models for their assistance in polishing the pre- sentation of the methodology section.
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Reviews:
[{"review_id": "lZ8Y253z7U", "reviewer": "Reviewer_TCjL", "summary": "The manuscript proposed the DispViT, a new architecture that directly regresses disparity from tokenized binocular representations using a single-stream Vision Transformer. The architecture achieves state-of-the-art accuracy on standard benchmarks.", "questions": "1. It would be better to provide the number of parameters in Tab 1 for a more comprehensive comparison.\n2. In Eq 3, does alpha_{ij} means attention weights?\n3. Would it be possible to present the quantitative experiment results on Middlebury and ETH3D?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The manuscript is well written. And the related work is comprehensive and clear.\n2. The comparison between the matching-centric paradigm and the regression-centric paradigm is an interesting topic.", "weaknesses": "1. Although the manuscript claims that the DispViT avoids constructing cost volumes, the shift-embedding tokenizer, which horizontally shifts the right view with a set of predefined offsets, is similar to the procedure of building cost volumes.\n2. Another concern is whether the design is effective. As shown in Tab 1, the DispViT (without the refinement module from NMRF) achieves the worst accuracy than all methods except for RAFT-Stereo. Do the experiment results mean that the previous works would provide a more reliable regression prior than DispViT if they are also refined by NMRF?  Moreover, the Tab 2 shows that the DispViT relies heavily on pretrained weights from additional data. However, some matching-centeric methods are trained from scratch. Therefore the comparison with matching-centeric methods might be unfair.", "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": "J6lDcVAj8b", "reviewer": "Reviewer_SVTB", "summary": "The authors propose DispVIT, a vision transformer for disparity regression. The key idea is to eliminate the left-right correlation step in stereo disparity estimation frameworks. The authors propose the use of shift-embeddings to encode multiple hypotheses by shifting the right image, an asymmetric initialization, a disparity-aware rotary positional embedding, and a probabilistic disparity parametrization. Experiments show that the proposed frameworks achieve competitive results with leading stereo networks.", "questions": "See weaknesses.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 2, "strengths": "The idea of eliminating the matching step is not new (DispNet), but this is the first attempt to execute a similar paradigm using ViTs. The design choices are clever and lightweight, and maintain the low-cost spirit of the proposed framework. The authors have conducted numerous experiments to evaluate their proposed method, and there are enough qualitative and quantitative results to draw conclusions. The paper is well written and presented. Strong quantitative results.", "weaknesses": "Major weaknesses \n\n1) The parameters for the right image shift are not really explored in the paper. It is unclear why a K of 8 is the best parameter for the shift. \n2) The proposed shift and summation of features to generate the right image tokens doesn't come with any scientific reasoning, intuition or physical justification. On the contrary, this operation removes any epipolar and geometric constraints.\n3) Following on point 2, the paper could benefit by a direct comparison with a monocular metric depth framework. \n4) Based on the experiments, the proposed approach relies heavily on DAv2 with an attempt to utilize the probabilistic disparity parametrization to scale the depth. This implies that the network might learn depth priors instead of utilizing the right image to compute metric depth. In lines 362-364 the authors omit to present the results of zero-shot generalization from scene flow to real datasets; however, such results would strengthen the claims in the paper, showcasing that the network learns to utilize the right image cues.\n\nMinor weaknesses \n\n1) What are the effects of stacking the refinement network more than 2 times? \n2) It is unclear from the text if DA-RoPE is just RoPE on both axes.", "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": "gEgYyXh3t5", "reviewer": "Reviewer_3E7R", "summary": "In this paper, the authors devleop a single-stream transformer (termed DispViT) for direct stereo disparity regression. Instead of explicit calculation of matching cost, DispViT directly regresses dispaity from tokenized binocular representations. To this end, a shift-embedding stereo tokenizer is developed and disparity-aware rotary position embeddings (DA-RoPE) is introduced. Experiments are conducted on KITTI 2012 and KITTI 2015 for performance evaluation. Results show the suprior performance as compared to the approaches included for comparison.", "questions": "See Weaknesses", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The proposed method technically sounds.\n- The motivation is clear and this paper is easy to follow.", "weaknesses": "- My major concern is about the technical novelty. Incorporating the powerfull learning capability of transformers for stereo matching (e.g., [c1]) has been studied for years. Compared with these methods, the proposed method is incremental. From this point of view, the contribution of this paper is rather limited.\n[c1] Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers\n\n- The experiments are insufficient. The cross-dataset generalization is a critical metric to assess the performance of a stereo matching model. Consequently, more datasets should be included for evaluation, including Middlebury and ETH3D. Currently, only results on KITTI 2012 and KITTI 2015 are reported, which is insufficient.\n\n- Several important SOTA methods are missing ([c1-c2]). These recent methods should be included for comparison to better demonstrate the superiority of the proposed method. \n\n[c2] Monster: Marry monodepth to stereo unleashes power\n[c3] Defom-stereo: Depth foundation model based stereo matching\n\n- The computational efficiency is also critical to the practicality of a stereo matching model. Consequently, the computational cost (e.g., FLOPs, memory cost and runtime) should be included for evaluation.\n\n- For shifted embedding, the maximum disparity range should be manually predefined, which requires prior information of the scene. I wonder whether the proposed method may suffer performance degradation under large disparities (e.g., >200 pixels on SceneFlow).", "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": "0zCP74UBrG", "reviewer": "Reviewer_U7kf", "summary": "1. A single-stream ViT framework that bypasses explicit matching to directly regress stereo disparity from tokenized binocular representations.\n2. A disparity-aware rotary position embedding method is proposed to remaingeometry-consistentevenat large\n displacements. \n3. SoTA stereo matching accuracy is achieved on the KITTI benchmarks.", "questions": "1. Please provide the changes in feature dimensions about the shift-embedding tokenizer.\n2. Please provide ablation studies about the refinement module. \n3. Please explain the less comparative performance in the foreground regions on the KITTI 2015 benchmark.\n4. Please revise this figure to use a single, unified colorbar across all methods within each scene in Fig. 2.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. SoTA stereo matching accuracy on the public stereo matching benchmark.\n2. Good real-time performance benefit from the single-steam framework. \n3. the proposed positiinal embedding method and shift-embedding tokenizer are novel.", "weaknesses": "1. Is the shift-embedding tokenizer designed to generate multiple tokens for the right view, or a token with a single channel that contains multiple groups with each corresponding to a predefined horizontal shift? The formula (2) is too simplified. Please provide the changes in feature dimensions during this process to help readers better understand it. \n2. The refinement module from NMRF-Stereo is not included in the ablation study. Thereby weakening the effectiveness of the originally proposed modules and methods.\n3. The disparity colorbar in Figure 2 appears to be inconsistent across different methods, especially for the NMRF results in the third row. Please revise this figure to use a single, unified colorbar across all methods within each scene", "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)