RobIA: Robust Instance-aware Continual Test-time Adaptation for Deep Stereo
Authors: Jueun Ko
Hyewon Park
Hyesong Choi
Dongbo Min
Venue: NeurIPS 2025 poster ()
Keywords: continual test time adaptation
3D computer vision
Primary area: applications

Abstract: Stereo Depth Estimation in real-world environments poses significant challenges due to dynamic domain shifts, sparse or unreliable supervision, and the high cost of acquiring dense ground-truth labels. While recent Test-Time Adaptation (TTA) methods offer promising solutions, most rely on static target domain assumptions and input-invariant adaptation strategies, limiting their effectiveness under continual shifts. In this paper, we propose RobIA, a novel Robust, Instance-Aware framework for Continual Test-Time Adaptation (CTTA) in stereo depth estimation. RobIA integrates two key components: (1) Attend-and-Excite Mixture-of-Experts (AttEx-MoE), a parameter-efficient module that dynamically routes input to frozen experts via lightweight self-attention mechanism tailored to epipolar geometry, and (2) Robust AdaptBN Teacher, a PEFT-based teacher model that provides dense pseudo-supervision by complementing sparse handcrafted labels. 
This strategy enables input-specific flexibility, broad supervision coverage, improving generalization under domain shift. Extensive experiments demonstrate that RobIA achieves superior adaptation performance across dynamic target domains while maintaining computational efficiency.

RobIA: Robust Instance-aware Continual Test-time Adaptation for Deep Stereo
Jueun Kol+
Hyewon Park
Hyesong Choi²
Dongho Min
Ewha Womans University Soongsil University
(jueun. ko, ko, hspark)Oesha. ac ac.kr byesong@ssu. ac. kr
dbmin@ewha.co ac ac
.rr
Abstract
Stereo Depth Estimation in real-world environments poses significant challenges due to dynamic domain shifts, sparse or unreliable supervision, and the high cost of acquiring dense ground-trute -trth labels. While recent Test- Time Adaptation (TTA) methods offer promising solutions, most rely on static target domain assumptions and input- invariiiv adaptation strategies, limiting their effectiveness under con- tinual shifts. In this paper, we propose RobIA, a novel Robust, Instance-Aware framework for Continual Test-Time Adaptation (CTTA) in stereo depth estimation. Mixture-of--xperts (AuEE-Moo)), parameter-efficient module that dynamically routes input frozen experts via lightweight self-attention mechanism tailored to epipolar geometry, and (2) Robust AdaptBN Teacher, a PEFT-based teacher model that provides dense gseudo-superviin by complementing sparse handcrafted labels. This strategy enables input-specifer flexibility, broad supervision coverage, improving generaliza- tion under domain shift. Extensive experiments demonstrate that RoblA achieves superior adaptation performance across dynamic target domains while maintaining computational efficiency.
RobIA integrates two key components: (1) Attend-and-
1 Introduction
Stereo Depth Estimation (SDE) fundamental task 3D scene understanding, with applications in autonomous driving and robotics. While deep learning-based stereo approaches have achieved notable accuracy improvements [[2. their success relies largely on supervised training with dense ground-truth disparity maps, which are costly and labor-intensive to obtain. As a result, they typically pre-trained on large-scale synthetic datasets [2] and later adapted to real world training datasets. However, they suffer from domain shifts by challenging conditions unseen in the training datasets, leading to performance degradation during inference. To deal with these challenges, Test- Time Adaptation (TTA) has recently emerged (16), aiming to adapt model on-the- fly to unseen target domains during inference, generally conducted in an unsupervised manner.
Current TTA approaches for SDE 7 g operate under the assumption of single, stationary target domain, overlooking more realistic scenarios where the domain distribution evolves over time, including changing weather, lighting, or scene structure. Continual Test-time Adaptation (CTTA) [9] 12] has recently emerged as a framework for continuously adapting models to consistently evolving target domains, and has been applied other vision tasks such classification and semantic segmentation. this regard, we explore more practical and challenging CTTA setting for SDE. In general, CTTA introduces two major challenges: catastrophic forgetting. where the model gradually loses the knowledge acquired from source domains while adapting to new target domains, and error
"quual contribution. Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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accumtlation, where noisy pseudo-labels progressively degrade model performance. Addressing these issues requires a careful balancing between stability, to preserve prior knowledge, and plasticity, to accommodate new domain-specific variations.
To address the stability-plasticit trade-off, recent CTTA methods have explored Garameter-Ffffcienn (PEFT) strategies [10, [2]. which preserve the representation capacity of pre-trained backbones by freezing them, while enabling adaptation via small set of trainable components such as prompt and meta-networks. These approaches have shown promising results in classification tasks, but their effectiveness diminishes in more complex settings such as semantic segmentation [T3), which requires dense, spatially structured predictions. A key limitation lies in the input- invarinn nature of standard PEFT modules where adapters or prompts are fixed for all inputs, making difficult to capture instance-speciin variations [14 [5]. This underscores the need for PEFT methods that dynamically adapt to each instance, particularly under continual domain shifts.
In addition to architectural adaptability, the quality of supervision plays a crucial role in effective adaptation. Existing methods for SDE commonly rely on photometric consistency loss 7 or pscudo-labels generated by handcrafted stereo matching algorithms [16], which are considered relatively robust domain shifts [77. Prior works typically filter these pseudo-labels with confidence- based thresholding, since they are often unreliable in challenging regions such as occlusions, reflective surfaces, or low-texture areas. While this selective supervision improves pseudo- quality, introduces a critical drawback: the model is trained only on subset of the input target domains, leading over-reliance to confident pseudo labels and weak generalization uncertain or structurally complex regions. These observations highlight the need for more comprehensive supervision signals that can cover the full input distribution and mitigate the risk of pseudo- label over-reliance during continual adaptation.
To enable instance-aware adaptation and improve pseudo-superrision under dynamic conditions, we propose Robust, Iwstence-Aware CTTA approach, termed RobIA, which is tailored for stereo depth estimation in continually shifting domains. RobIA addresses the limitations of conventional PEFT and proxy-labeling approaches through two key components. First, we introduce the Attend- and-Excite Mfxture-of-Expeee (AttEx-MoE), compact yet effective MoE architecture that enables input-specific adaptation without updating the backbone. Inspired by selective channel excitation [18), AtEE--MoF dynamically activates the convolutional channel experts via self-attention, conditioned on instance-awand global features. To reduce computational overhead, we constrain the attention operation to be row-wise along epipolar lines, leveraging stereo geometry while preserving long- range contextual reasoning. This design allows AtEX-MoE to maintain the efficiency of PEFT while introducing fine-graing traiegga content-aware adaptability crucial for dense stereo predictions.
Second, we propose the Robust AdaptBN Teacher, a complementary PEFT-based model that enhances the coverage of pseudo-labels during adaptation. Prior work B 19 has presented an effective test- time adaptation mechanism by updating only the affine parameters of batch normalization layers, known as AdaptBN 20 21]. Building on this, we leverage an AdaptBN- -trainnd teacher model to complement the sparsity of handcrafted stereo pseudo-labed in low-confidence regions. Specifically, we adopt a hybrid supervision scheme: reliable pseudo-labet from handerafted stereo matching algorithms are used in high-confidence areas, while predictions from the Robust AdaptBN Teacher supervise cow-confidence regions. This dual-source guidance allows the model to retain the precision of proxy labels where they are reliable, while extending supervision coverage to previously ignored areas, thus promoting better generalization across the entire input space.
Our key contributions are summarized as follows: (1) We propose RobIA, novel CTTA framework specifically designed for stereo depth estimation under dynamic domain shifts. (2) We introduce a parameter-efficienn, instance-awar adaptation module (AtEE--MEE) that dynamically routes input through frozen convolutional experts using a lightweight row-wise self-attention mechanism. (3) We design complementary PEFT-based (AdaptBN) teacher that provides neeuo-supervision in low-confidence regions, enhancing coverage and robustness of pseudo labels. (4) We a a dual- source supervision scheme that combines reliable handcrafted stereo pseudo-label with predictions from the AdaptBN Teacher, enhancing coverage and robustness of pseudo labels.
2 Related Works
Time-Time&de Adaptation for Stereo Depth Estimation Test-Time Adaptation (TTA) stereo depth estimation (SDE) aims to adapt model to new domains in an online or real-time manner without

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access to source data or ground-truth labels. Early approaches include modularized model update [8) meta-learning- baased adaptation 77. and pixel-wise focused adaptation [22]. Despite these advance- ments, prior stereo TTA approaches have mostly focused on single-domain adaptation or long-term adaptation using large sequences (typically each domain contains more than 2K frames) within a static domain. These approaches overlook practical scenarios in which domains evolve continuously over time. To address this gap, we introduce a continual test-time adaptation scenario for SDE that reflects temporally evolving domain distributions,
Self-supervised Learning for Stereo Depth Estimation A long-standing challenge in SDE the low density and high acquisition cost of ground labels. To overcome this, self-supervised learning has been widely adopted [23 24 commonly using the photometric loss between stereo pairs. However, this signal often suffers from matching ambiguities, such as occlusions and specular surfaces, leading to unreliable supervision. [24 exploited traditional stereo algorithms to generate pseudo-labels, filtering the outliers with confidence measures. [25] further introduced a monocular completion network to distill hard-to-match regions from stereo matching. While effective, it requires separate network and multiple inference steps, leading to significant computational overhead, making it impractical for online adaptation. In contrast, our approach leverages a robust teacher model with lower computational cost, offering reliable guidance even in challenging regions where handcrafted pseudo- labels are absent.
Cottture-of-Exxpert Mixture-of ofteerrtss-x (MoE) 26 27 has been widely used in various domains, including CTTA MoE enables dynamic selection of expert subnetworks via routing mechanism. making it effective for multi-task learning 28 29 and continual learning 30 31]. In CTTA, several studies have explored parameter-effiiien fine-tuning (PEFT) approaches that integrate MoE modules into pre-trained backbones [32 [33 enabling domain adaptation with minimal trainable parameters. However, most of this research has been conducted on Transformer-bart architectures, and the application of MoE within CNNs remains relatively unexplored. DeepMoE (34) is a common approach CNN-based architecture to treat individual channels as experts, allowing fine-grained modulation of feature representations and improving model sparsity. Our work is motivated by this underexplored direction. proposing a comocampai MoE design that supports instance-aware adaptation under continual domain shifts.
3 Preliminaries
Continual Test-time Adaptation In CTTA paradigm, we are given a model pre-trained on source dataset (Xs, Vs).. The goal is to adapt the model to multiple unlabeled target data distribution during deployment, where n represents the number of unseen domains. When the target domain data N1; consists of N; target samples for i = 1,..., n. for simplicity, we denote (I," I as the h target stereo image pair for 0, ..... |Xr Similarly, in the following sections of this paper, we omit the domain notation Xr, as all target domains are considered the stereo model predicts disparity map from single stereo image pair (T/ IRR), and updates its parameters before proceeding to the next input.
X7 = {Xr, NT....., Xr,
where |xr] >E - Nr-
be integrated into single sequence. Therefore, at each time step t,
Pseudo-supervision for SDE Since ground-truth labels are unavailable at test time, the model must be trained in self-supervised manner. Following prior work (17), we obtain the handcrafted disparity map Proxy as a pseudo-label using the traditional stereo matching algorithm, Semi-Globe Matching (SGM) [16]. Then, D prxxy is filtered by the confidence threshold e, where confidence o (p) is calculated via left-right consistency at the corresponding pixel p. Consequently. the mask Myalid which indicates the reliable region of D Prxxx (i.e. valid region), is denoted as follows:
{
if (p)) 2 otherwise
Mvatiap)
Mincaldd = 1 Myalid indicating the unreliable region of Dproxy (i.e. invalid region).
(MoE) to convolutional neural networks (CNNs) formulates C' convolutional kernels (i.e. the output channels of the previous layer) an individual expert E; for i = L,.., C, and introduces gating network G to compute
Mixture-of- -feeeerss-xxtsssee-t in CNN Prior work 34 applying Mixture-of-
3

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Figure The Overview of RobIA. During test time, the student model trained using dense pseudo- labels generated by combining sparse handcrafted proxy Panxy with Robust Teacher prediction Dreacher: ensuring stable adaptation under dynamic conditions. AttEx-MoE integrates row-wise self-attention router and gating network G into deep encoder blocks. The row-wise self-attention router extracts global context from an input feature map 2, which is subsequently processed by gating network. The student backbone is kept frozen, and only the router, gating network, and the regression parameters of the decoder are updated.
weighted combination of expert outputs. The output feature y defined
y
9: Ex(x),
where g E C is the gating values obtained from the gating network G, with Ji being the weight assigned the 2h expert (x). this case, the gating network G is implemented sparse gating network that takes shallow embedding of the raw input image and produces channel-wise activation score. G(e) is obtained by projecting the embedding e into a score vector via learnable weight matrix W followed by ReLU activation:
multi-headed
G(e) = ReLU(W, e).
(3)
This formulation enables input-dependent dynamic selection of feature channels, improving both model sparsity and representational flexibility.
Proposed Method
4.1 Motivation and Overview
CTTA presents several key challenges, notably catastrophic forgetting and error accumulation. Parameter-efficient fine-tuning (PEFT) methods address these issues by freezing pretrained weights, preserving source knowledge. However, in conventional PEFT-based CTTA approaches, efficient module that adapts to the target domain is added to the original feature, performing the uniform transformation all inputs. This rigid transformation is insufficient for handling various input variations, particularly in dense predictions under domain shifts. Moreover, pseudo-labels from handcrafted stereo algorithms are often consistently filtered in the unreliable supervision regions due to inherent matching difficulties. Despite their domain-agnostte properties, they lead to over-reliance reliable regions, misdirected adaptation elsewhere, and ultimately degrading overall performance.
To overcome these limitations, we propose novel CTTA framework, RobIA (see Fig. [p. RoblA designed to preserve the rich representational capacity of source knowledge while enabling stable and effective adaptation through the Attend-and-Excitt ffxture--ffExpprtt (AttEx-MoE)) architecture. This module extracts instance-specific features via row-wise self-attention router and dynamically

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excites frozen channel experts, allowing the model to adapt flexibly without modifying the pretrained backbone. To further address the challenge of biased and sparse pseudo-labels, we additionally introduce the Robust AdaptBN Teacher. It provides a hybrid supervision strategy that combines reliable handcrafted pseudo-labels with predictions from the robust teacher model.
4.2 Input-Aware Mixture-of-Experts via Attend-and-Exttt
Vixture-of-Experty (MoE) in CNNs consists of a shallow embedding network and gating network with ReLU activation treat each convolutional kernel an individual expert and introduce sparsity into the model. However, this approach presents two key limitations: (1) shallow convolutional layers used in the embedding network lack sufficient spatial context for optimal expert selection, and (2) ReLU-iddeedd sparsity limits representational capacity, especially when the backbone is frozen. To address these challenges, we propose Attend-and-Exeitt Mixture-of- Experts (AttEx-MoE), a novel MoE design tailored for PEFT-based adaptation in stereo depth estimation. AttEx-MoE enables instance-aware, channel-wise modulation under continual domain shifts.
Row-wise Self-Attention Router A key component of AtEx--MoE is the row-wise self-attention router, which extracts global context to guide instance-aware expert excitation. Since convolutions are effective at capturing local patterns but struggle with modeling long-range dependencies, prior work [8] identified this limitation and proposed global average pooling (GAP) to create channel-wise features. However, GAP has the drawbacks of treating all spatial positions uniformly and ignoring spatial importance. Therefore, we employ self-attention mechanism in the gating network to explicitly capture inter-channel dependencies and their spatial relationships. This allows Attend-and- Excite operations to adaptively excite the most relevant features for each target instance. Moreover, we apply ID self-attention along each epipolar line (i.e., row) of the feature map by leveraging stereo geometry [35], significantly lowering computational overhead while maintaining global context.
As shown in Fig. [ the encoder of our model consists of N blocks, with the AttEx-MoE module applied the final, deepest encoder block (i.e., at 1/32 resolutions) and the corresponding upsampling module connected via skip connections, In each block, the row-wise self attention router R; for i I, .... .V computes the gating input e from the input feature map z²-1, which is then passed the gating network G in each convolutional layer.
H
e ==
Crs
(4)
H
where e, = softmax k-kk
er is computed for E [1, using 4. = x, W Ki = z, Ws. and
2,- = 2, W.. Here, E R RWxx xC denotes the r-th row of the feature map 2 E RR xxxxxx x and the attention computed independently for each row. The attention score e, obtained along the epipolar line is averaged over the height dimension H to compute the final gating input e.
Expert Excitation via Sigmoid In 34]. each expert output Ex(x)) is scaled by a gate value G(a) produced via ReLU activations, which suppresses certain experts by zeroing their outputs, as explained in and 3 While these sparse expert outputs can enhance generalization (36], this sparsity may reduce expressivity when the backbone is frozen in the PEFT setting.
Therefore, we adopt Sigmoid-based soft gating mechanism, in contrast to the commonly used ReLU. This design allows all experts to be activated to varying degrees rather than enforcing hard selection of subset of experts, promoting richer expert combinations and diverse representations conditioned on each input instance. We find that soft gating is particularly effective in the PEFT setting (See Tab. 5). it maximizes the utilization of all available experts with limited learning capacity. The gating mechanism is defined with the Sigmoid activation a as:
G(e) a (Wg e).
4.3 Dense Pseudo-label Generation via Dual Supervision
For effective adaptation to the target domain, the design of the supervision plays a critical role. Pseudo-labels generated by traditional stereo algorithms are typically sparsified through reliability- based thresholding [17,, which leads to over-reliance on sparse supervision and consequently limits performance gains in CTTA. To address this, we propose a dense pscudo-label generation strategy that
5

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combines the domain-agnnn agnostic reliability of handcrafted pseudo-labels with the learning capability of learnable teacher model.
adapts only the affine parameters (scale 7, shift (8) of batch normalization layers, enabling low-dimensional, channel-wise feature modulation and stable adaptation. We leverage the AdaptBN-based teacher model to complement unreliable regions in sparse, handcrafted pseudo- labels, producing dense pseudo-labels as supervision for student model. This mitigates the student's over-reliance on sparse pseudo labels and reduces performance degradation in unreliable regions.
AdaptBN 3
20 21
Why AdaptBN? Most CTTA frameworks ( avoid stochastically uppputtt teacher model, as is expected to provide stable supervision with minimal computational and memory overhead. Accordingly, common CTTA designs adopt Mean Teacher models updated via exponential moving average (HMA) 37 or fixed, source-trained models [38] However, stereo depth estimation task presents unique setting where handcrafted stereo algorithms can yield reliable pseudo-labels (i.e. proxy), reducing the need for stability-focusee teachers. In this context, the teacher must not only stabilize but also generalize beyond the limitations of the sparse proxy, particularly in regions where handcrafted labels are unreliable.
Since AdaptBN performs adaptation via low-dimensional affine transformations, it offers controlled yet expressive adaptation mechanism for test-time tist-time adaptation. This makes AdaptBN spatially robust, allowing the teacher to effectively adapt the target domain while reducing the model's over-reliance to the sparsely provided proxy labels. In contrast, EMA-based teachers are less suitable in this context. Although they maintain stability by updating weights gradually, they prone when the student produces biased predictions. In such cases, EMA teachers tend to reinforce incorrect predictions, failing to provide correct guidance to the student [39] We further validate these claims through analysis and ablation studies, which demonstrate AdaptBN teacher's ss contributions to both stability and adaptive refinement, especially in regions where handcrafted labels are unreliable.
accumulation
4.4 Continual Test-time Adaptation Process
Model Initialization We insert the lightweight AttEx-MoE module into source-trained base model. Following recent CTTA studies 32). train the AttEx-MoE module the labeled source dataset while keeping the backbone frozen. This short warm-up phase allows the MoE module to learn instance-aware routing behavior without altering the core representations, providing stable initialization for CTTA. During the supervised warm-up phase, we trained the model in the same way as DeepMoE [3). For test-time adaptation, freeze the base network and only update the AttEx-MoE module and the regression parameters of the decoder.
Total Loss The overall loss function is as follows:
L farosy + XLreacher
(6)
Lproxy- = Mvalid smoothrs (D Dproxy - D presd),
(7)
Leacher Minvalid smooth ( Deeache D preu))
(8)
where D Proxxy and Drescher denote the pseudo-labels generated by the handcrafted stereo algorithm [66
and the AdaptBN teacher model, respectively. The predicted disparity map Dped is supervised by two loss terms, Lprcxy and Lrencher, corresponding to each pseudo-label source. Incorporating supervision from Dreacher helps regularize the student model, mitigating over-re werrreliance sparse handcrafted labels and preventing performance drops in regions lacking reliable secudo-labels. À is a loss weight, which controls the influence of teacher predictions in our dense pseudo-label formulation.
5 Experiments
Datasets. To simulate TTA and CTTA scenarios, following prior work (8) all experiments were conducted on well-renowned stereo benchmarks, including KITTI RAW 40) DrivingStereo [T], and DSEC [42). These datasets cover various conditions, such as different weather scenarios and urban cityscapes in both daylight and nighttime. The synthetic Flyingthings3D, part of the synthetic SceneFlow dataset (2) was used to pretrain the stereo model before test time. Sparse pseudo- labels D Drecy were obtained via Semi-Globa Matching (SGM) [6), followed by a left-right consistency check. The effective label density varies across datasets- datasets-roughlyy 92% for KITTI RAW, 72% for DrivingStereo, and 45% for DSEC, highlighting the need for robust dense supervision, especially in relatively sparser datasets such DrivingStereo and DSEC.

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Table 1: Performance comparison of Continual Test-time Adaptation on DrivingStereo benchmark over 10 rounds. To save space, only 1st and 10th round scores are written. Bold denotes best and AT denotes our method with dense pseudo-label D Dencher-
Round Condition
4 Mean DI-ail
dusky DI-all
cloudy DI-ail
rainy EPE
dusky DI-ail
cloudy D1-all
rainy DI-all
Method
Adapt.
EPE
EPE
DI-all
EPE
EPE
EPE
EPE
MADNet 55
(a) no adapt.
13.24
1.69
6.56
1.22
11.51
2.18
13.24
1.69
6.56
1.22
11.51
2.18
10.44
1.70
(b) FT
5.08
1.06
4.82
1.09
6.4
1.43
6.04
1.66
5.85
2.06
7.13
1.9
6.13
1.73
(c) MAD++
6.46
1.15
4.36
1.04
6.01
1.29
5.79
1.60
6.04
2.01
7.28
1.89
5.86
1.41
CoEx 45
(d) no adaps.
5.53
1.14
3.55
0.99
7.61
1.64
5.53
1.14
3.55
0.99
7.61
1.64
5.56
1.26
(e) AdapiiN
5.16
1.11
3.13
0.93
6.14
1.41
2.71
0.85
2.36
0.8
3.32
1.03
3.08
0.94
(1) FT
5.25
1.11
2.98
0.91
5.81
1.37
3.05
0.88
2.48
0.81
3.63
1.09
3.04
0.92
(8) FT AT
5.09
1.1
3.01
0.91
5.83
1.38
2.63
0.84
2.33
0.79
3.08
0.99
2.93
0.91
Eco
12
(b) MetaNet
4.61
1,05
2.89
0.89
4.21
1.17
3,42
0.93
2.69
0.87
4.46
1.26
3.07
0.95
RobLA (ours)
(i) AtEEx-MoE:
4.01
1.01
2,4
0.84
4.44
1.13
2.72
0.88
2.29
0.84
3.89
1.22
2.98
0.97
() Anlix MoE AT
4.28
1.03
2.4
0.84
4.54
1.16
2.4
0.84
2.24
0.82
3.02
1.00
2.77
0.91
For CTTA, we constructed new benchmark by sampling 500 frames per domain from existing TTA sequences, constructing a short sequence with frequent domain shifts. Each cycle consists of 3-4 domains and is repeated over 10 rounds to simulate long-term adaptation with recurring conditions. Specifically, we obtained sequence of dusky wec--oooooyyraaaao
for DrivingStereo, night1 to night4 for DSEC. and city diee-eresieeniall -residentall-cammus---- -cwmpus-mmsttta -road for KITTI RAW. Unlike prior works that simulate long- term shifts over 44K frames (17]. our CTTA setting imposes more rapid domain adaptation within short sequences. reflecting real-world constraints where environmental changes occur frequently and previously seen conditions may reappear. We include TTA results for all datasets, additional results on CTTA, and ablation studies on pseudo-supervision in the supplementary material.
Implementation Details. We use CoEx [43], a compact and real-time stereo network using Mo- bileNetV2 [44] backbone, our base architecture. Following prior work 45), we retrained the model on the synthetic source datasets with strong data augmentations to improve generalization. All experiments were conducted on NVIDIA A6000 and RTX 3090 GPUs and further implementation details, including hyper parameters, are in the supplementary material.
no adapt. denotes the source-trained model without adaptation. We additionally evaluated two variants of MADNet2 [45]: FT, which updates all model parameters, and MAD++, which applies modular updates. All PEFT-based CTTA methods, including AdaptBN, MetaNet, and proposed ArEx-MoEE, were implemented on top of the CoEx for a fair comparison. AdaptBN tunes the affine parameters of batch normalization layers, MetaNet tunes the meta network from EcoTTA [12), and AuEE-MMo updates the lightweight gating module for expert selection. In all cases, the decoder's regression parameters were jointly updated. AT' setting uses dense pseudo-labels generated from our AdaptBN teacher.
Evaluation Metrics. We reported End-Point Error (EPE) and DI-all that measures the percentage of pixels with absolute disparity error exceeding pixels and 5% of the ground truth. We adopt the standard online adaptation protocol: the model predicts each frame before updating, then uses that frame to adapt before moving to the next, reflecting deployment without access to ground truth.
5.1 Main Results
CTTA Experiments. Tab. presents the 10-rounds CTTA performance on the DrivingStereo. Our method (h) consistently outperforms all baselines across different weather conditions and adaptation rounds on DrivingStereo. Compared to MADNet-based experiments (a)-(c), which is sfate-of-the-an stereo TTA method, our approach yields substantial improvements in all domains (dusky, cloudy, rainy), demonstrating stronger generalization and adaptability under continual shifts.
While full tuning approach (f) achieves performance gains in the early rounds of the experiment, suffers from performance degradation due to error accumulation and forgetting over time. Parameter- efficient tuning methods including (e) and (h) tend to preserve source knowledge more effectively, leading to more stable performance across rounds. However, these approaches often exhibit limited adaptation performance due to the restricted capacity of parameter-efficien tuning. For instance, (h) EcoTTA-whie adapts meta network-shows reduced effectiveness structured prediction tasks such
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Table 2: Performance comparison of Continual Test-time Adaptation on DSEC benchmark over 10 rounds. To save space, only 1st and 10th round scores are written. Bold denotes best and AT' denotes our method with dense pseudo-label D Deacherr.
Round
4 Mean
Condition Adapt.
Night#1
Night#2
Nighta3
Night#4
Night#1 DI-all EPE
Night#2
Nightk3
Nightt4 DI-ail EPE
Method
DI-all EPE DI-ail EPE D1-all EPE D1-ail EPE
DI-ail EPE
DI-ail EPE
D1-ail EPE
MADNet
(a) no adapt.
8.38
1.80
14.71
2.37
11.00
1.86
11.82
1.85
8.38
1.80
14.71
2.37
11.00
1.86
11.82
1.85
11.48
1.97
FT (c) MAD++
4.7
1.24
7.79
1.49
5.97
1.31
6.33
1.31
3.57
1.11
7.34
14
5.62
1.25
588
1.24
5.71
1.27
5.52
1.34
8:43
1.53
6.21
1.34
6.66
1.33
3.89
1.16
7.53
1.42
5.7
1.27
5.89
1.27
6.04
1.32
CoEx
(d) edapt.
6.10
1.38
12:24
1.94
8.34
1.58
8.05
1.50
6.10
1.38
12.24
194
8.34
1.58
8.05
1.50
8.68
1.60
(e) AdaattBN
4,96
1.22
8.47
1.49
4.59
1.11
4.67
1.09
2.97
1.04
6.07
1.27
4.32
1.1
4.45
1.11
4.54
1.13
4.99
1.23
8.41
1.48
4.66
1.12
4.67
1.1
3.00
1.04
6.24
1.28
4.44
1.11
4.57
1.12
4.59
1.13
(g) FT AT
5.11
1.24
8.76
1.52
4.73
1.13
4.73
1.1
2.87
1.01
5.86
1.24
4.02
1.06
4.15
1.07
4.38
1.10
EcoTTA
(b) MetaNet
4.36
1.17
7.17
1.39
4.69
1.13
5:33
1.18]
3.46
1,08
6.71
1.36
4.71
1.14
5.28
1.19|
5.13
1.21
RobIA (ours) (i) AnEx-MoE
4.33
1.17
7.92
1.46
4.38
1.1
4.79
1.12
3:00
1,03
5.69
1.23
4.23
1.09
4.61
1.11
4.47
1.12
(i) AnEx Mol. + AT
4.45
1.17
8.18
1.48
4.75
1.15
4.97
1.12
2.96
1.01
5.65
1.22
4.11
1.05
4.34
1.08
4.46
1.11
as stereo depth estimation. In contrast, our approach (i) AttEx-MoE enables input-dependent expert routing and selective feature excitation, achieving both robust and adaptive test-time performance, leading to improved adaptation accuracy.
Incorporating dense pseudo-labels via the AdaptBN teacher (denoted AT) further enhances long-term stability. As shown in 10th -ouud results, models trained with dense supervision ((g) and (i)) exhibit notably lower error rates after long-term adaptation compared to their sparse-only counterparts. This underscores the importance of broader supervision coverage, our dual-source dense pseudo- label strategy helps prevent the model from focusing exclusively on the reliable regions of sparse handcrafted labels.
Tab.
reports results on the DSEC benchmark, which includes challenging nighttime conditions. Our method again outperforms MADNet2 and other baselines, maintaining strong adaptation performance. Notably, both FT and MoE variants benefit from AdaptB supervision, showing improved accuracy and stability by round 10. For instance, in Night#44, D1-all error drops from 4.57 to 4.15 in (f) and (g). and from 4.61 to 4.34 in (i) and (j), respectively. Moreover, these results highlight that AttEx-MoE tuning provides greater adaptability than other PEFT-based methods ((c) and (h)), while achieving comparable performance to full model tuning with improved efficiency.
Table 3: Performance comparison of Continual Test-time Adaptation on KITTI RAW benchmark over 10 rounds. To save space, only 1st and 10th round scores are written. Bold denotes best and AT denotes our method with dense pseudo-label D Dreacher-
Round
10
All
Condition Adapt.
City
Residential Campus (K3) (x3)
Road D1-all EPE:
City
Residential Campus (x2)
(x2)
Road
Mean
Method
D1-all EPE D1-all EPE D1-all
HPE
D1-all EPE
D1-all EPE D1-all
EPE
D1-ail EPE
D1-ail EPB
MADNet 2
(a) no adapt.
3.83
1.08
3.06
1.09
5.43
1.12
3.34
1.05
3.83
1.08
3.06
1.09
5.43
1.12
3.34
1.05
3.92
1.09
FT MAD++
1.21
0.92
0.96
0.93
2.06
0.82
1.12
0.86
0.91
0.88
0.79
0.91
1.56
0.75
1.01
0.84
1.13
0.85
1.51
0.98
0.95
0.94
2.28
0.87
1.22
0.88
0.92
0.87
0.85
0.92
1.72
0.77
1.08
0.88
1.24
0.88
CoEx
adapt.
2,08
0.99
1.75
0.96
2.89
0.96
2.79
1.00
2.08
0.99
1.75
0.96
2.89
0.96
2.79
1.000
2.38
0.98
(e) AdaptMN
1,09
0.86
0.82
0.9
1.5
0.74
1.05
0.85
0.66
0.84
0.72
0.89
1.25
0.7
0.86
0.82
0.91
0.82
(f)
0.86
0.92
1.33
0.97
0.86
0.84
0.90
1.16
0.71
0.81
0.51
0.86
0.82
FT AT
1:06
0.87
0.79
0.91
1.27
0.72
1.03
0.85
0.55
0.84
0.6
0.89
1.13
0.7
0.8
0.82
0.82
0.82
EcOTTA
MetaNet
0.94
0.87
0.96
0.92
1.65
0.77
1.29
0.87
1.24
0.92
1.17
0.95
1.78
0.83
1.69
0.91|
1.33
0.88
RobIA (ours) Allx-MoEE
0.88
0.92
1.45
0.86
0.85
0.89
1.32
0.74
0.83
0.84
(i) AnEi MoE. + AT
1.21
0.89
1.04
0.92
1.51
0.75
1.41
0.86
0.78
0.86
0.77
0.89
1.36
0.75
1.19
0.84
1.09
0.84
Tab. reports the CTTA performance on the KITTI RAW. Our method outperforms MADNet2, a state-of-the-ee stereo TTA method, maintaining strong adaptation performance throughout the experiment. the case of KITTI RAW, more than 90% of the handcrafted pseudo-labels are considered reliable, as the dataset primarily consists of daytime urban scenes that are less challenging for stereo matching compared to weather-affected or nighttime scenes. This leads to relatively stable adaptation compared to other benchmarks. However, (g) FT AT benefit from the additional dense
8

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Mean D3
Mean D1
20 Round
Round
Total Region
Valid Region
Invalid Region
Figure 2: D1-all error rate in different pseudo-label regions. D1-all error rate 10 adaptation rounds in different pseudo-label regions. We separate evaluation into (left) the entire image, (middle) regions with valid handcrafted pseudo-labels, and (right) regions without reliable supervision (invalid).
pseudo-labels, which improve model adaptability by further expanding the supervision coverage, resulting in better performance by round 10.
5.2 Analyses
Performance degradation due to Sparse Pseudo-labels. To understand the limitations of sparse handerafted pseudo-labels, evaluate model performance under continually shifting weather conditions by separately analyzing regions with reliable (valid) and unreliable (invalid) pseudo-labels with higher learning rate(2e-6). As shown in Fig. 2 models trained solely with sparse supervision show a stable reduction in error within valid regions but suffer from sharp performance degradation in invalid regions- even when revisiting previously seen domains.
In contrast, the model adapted with AdaptBN progressively exhibit consistent improvement across both valid and invalid regions
time. Notably, when incorporating dual-source dense pseudo- labels (denoted as AT), we observe that the error rate invalid regions, which previously increased due to lack of supervision, is significantly mitigated. This indicates that the AdaptBN teacher provides reliable supervisory signals even in regions previously lacking ground-truth guidance, effectively supporting generalization across the entire image.
Table 4: Computational Cost Analysis. The aver- age computational cost and error rate during 10 round experiments of DrivingStereo dataset.
Table 5: Ablation study on AtEEMMo Architecture. The average DI-all and EPE error rate during round experiments of DrivingStereo dataset.
Mean. Di-all EPF
Mean.j Di-ail EPR
Method
Adapt.
#. Trainable Mem. Runtime
Ronter
Activation
(M)
(MB)
(ms)
(%)
(px)
Shallow Finhedding
ReI.I
3,16
0.96
2
(a) no adapt.
179
11
10.44
1.70
Sigmoid
3.11
0.96
FT (c) MAD++
3.22
3,22
398
18
5.86
1.41
GAP
ReLU
4.06
1.06
Sigmoid
3.27
0.95
CoFx
(d) adapt.
694
23
5.56
1.26
(c) AdaptBN
0.03
2704
139
3.08
0.94
Self attentitenion
ReL.I.
3.61
1.13
FT
2.73
2744
255
Sigmoid
3.09
(g) FT EMA
2.73
2835
267
2.96
0.92
Column wise self-attention
ReL.I.
4.11
1.07
(b) FT
2.79
5469
378
2.93
0.91
Sigmoid
RobIA (ours) (i) AttEx-MoE
1.19
1392
97.34
2.98
0.97
Row-wise Sclf-atteution
ReLU
3.77
1.09
AllEx MoE AT
1.22
4096
204
2.77
0.91
Sigmoid (ours)
2.98
0.97
Computational Cost Tab, 4 provides a comparative analysis of computational cost and adaptation performance. All runtime and memory measurements were recorded on an NVIDIA RTX 3090 GPU. MADNet2, while designed for test-time efficiency, shows consistent performance degradation under continual domain shifts, indicating limited robustness in dynamic settings. contrast, our AttEx-MoE tuning (i) achieves comparable superior accuracy with approximately half the number of trainable parameters and reduced memory usage compared to full model tuning methods such as (f) FT and (g) FT + EMA. (j) achieves the best overall mean D1-all and EPE, while maintaining reasonable computational cost, demonstrating the effectiveness of input-aware expert selection resource-efficient adaptation.

---

Table 6: Ablation study on X.
ATL
dusky DI-all EPE
cloudy D1-all EPE
rainy DI-all
dusky DI-all
clondy D1-ail
rainy EPE
Mean
EPE
EPE
EPE
D1-ail
DI-all
EPE
(a) 0.05
4,31
1.03
2.54
0.85
4.16
1.07
3.29
0.9
2.45
0.83
3.25
1.03
2.97
0.91
(h)
0.84
1.16
1.00
2.77
0.2
4.56
1.05
2.75
0.88
4.76
1.17
2.69
0.85
2.23
0.8
2.82
0.96
2.84
0.90
(d) 0.3
1.07
2.88
0.89
5.39
1.27
2.44
0.83
2.14
0.8
2.84
0.95
2.89
0.91
5.3 Ablation Studies
AttEx-MoE architecture. Tab. 5 presents an ablation study on the architectural components of AuEi-MoE. We compare different gating network designs, including the type of router and the activation function (ReLU vs. Sigmoid), under the same training setting.
Shallow embedding and GAP-based routing result in higher error rates, supporting our observation that these methods lack sufficient spatial context for precise expert selection. Self-attention routers improve performance, but full 2D attention introduces additional computation without significant gains. Although column-wiss wiie self-attention does not leverage epipolar geometry, it partially pre- serves spatial structure and maintains comparable efficiency. However, its higher error rates further validate our design choice of row-wise attention. ReLU comivttion-commonly used enforce opansity-coniiseently underperforms across all router types, likely due to reduced expressivity under frozen backbones in the PEFT setting.
Our final design, combining row-wise self- atteetteeno with sigmoid-basee expert excitation, achieves the best overall results. The router effectively captures structured global context along epipolar lines, and sigmoid activation enables more expressive and stable expert modulation. These results confirm the effectiveness of our Attend-and-Exoen design for robust and efficient instance-aware adaptation.
Loss weight for Lreacker We ablated the loss weight À in Tab. 6 As shown in Tab. 6 we evaluated range of À values over 10 CTTA rounds the DrivingStereo sequence, using AuEx-MoE as the student model. The best performance is achieved with (b) = 0.1, which is also adopted the main experimental results. Since the teacher model needs to newly adapt to the target domain and the effect of proxy supervision is relatively more important in the early stages, the larger À values tend to limit the model's adaptability the early rounds of the adaptation. However, teacher predictions become more accurate over time, the larger À leads to better performance later rounds. In contrast, smaller values behave similarly single-source supervision, resulting in higher error rates at the end of adaptation.
Conclusion
We presented RobIA, robust and instance-aware framework for continual test-time adaptation (CTTA) in stereo depth estimation. RoblA addresses key challenges posed by dynamic domain shifts and sparse supervision through two core components: AuEExMMFE, lightweight Mixture- of-Fxperts module guided by epipolar-aware self-attention, and Robust AdaptBN Teacher that complements handcrafted pscudo-labels for generating dense supervision. This design enables flexible, input-specific adaptation while maintaining computational efficiency. Extensive experiments across dynamically shifting target domains demonstrate that RoblA consistently outperforms existing methods, highlighting the importance of instance-awe aware adaptation and hybrid supervision strategies for reliable deployment of stereo depth models in real-world settings.
Limitations and Future Work. While RobIA demonstrates strong performance in CTTA, it has cer- tain limitations. Although AttEx-MoE offers input-aware adaptation, reliance on predefined expert structures may limit flexibility in highly heterogeneous environments. Future work includes online expert refinement to further improve adaptation performance long-term deployment scenarios.
Acknowledgements
This work was supported by ITTP grant funded by MSIT (No. RS-2022-00155966: AI Convergence Innovation Human Resources Development (Ewha Womans University) and No. RS-2021-1121 AI Innovation Hub).
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A Implementation Details
For the warm-up process, we trained the model using the Adam optimizer for 10 epochs with fixed learning rate of Se-4, During test-time adaptation (TTA), we also used Adam optimizer across all methods. The learning rate was set 5e-6 for training the AdaptBN teacher model and le-5 for MADNet2 [45]. Tab. reports the learning rates of student models used in experiments. For full-tuning methods, we used relatively smaller learning rates rates-aatpaae approximately 10 times lower than those used in efficient tuning methods such as AdaptBN, MetaNet [12], and our AttEx-MoE. This choice is motivated by the observation that large learning rates full-tuning settings significantly impair generalization performance.
Our baseline model is CoEx [43] compact and real-time stereo matching network. The feature extractor in CoEx is composed of four scale levels, with upsampling modules built using long skip connections each scale. To implement EсоTT [12] in the CTTA setting for stereo matching, we inserted its meta network into the MobileNetV2 backbone of CoEx at four scale-level blocks (K=4).
Table 7: Learning rates.
Dataset
Exp.
Learning Rate
Full tuning
DrivingStereo
CTTA
5e-7
DSEC
CTTA
2c-6
KITTI RAW
CTTA
le-5
Efficient tuning
DrivingStereo
CTTA
5e-6
DSEC
CTTA
3c-5
KITTI RAW
CTTA
le-4
B Additional Analysis and Ablation Studies
visualizes the pseudo-labels generated by different methods after ten rounds of adaptation from the rainy sequences of the DrivingStereo dataset. On each, reported disparity maps of the student model and the corresponding error rate. Note that, for sparse pseudo-labels, we only measured error for regions where both the pseudo-label and the ground truth are valid.
Pseudo-label visualization. Fig- 3
The handcrafted sparse pseudo-labels often lack supervisory signals in challenging regions for stereo matching, such as reflective surface, low-texture regions, or occlusions. Furthermore, this issue extends to image borders, where the hand-crafted stereo algorithms (e.g., SGM) often struggles due
D1-all:2.289 22%2
(a) Left image
(b) Sparse pscudo-label
(c) Dense pseude-label
D1-ail:2.319 31%
D1-ail:2.23%
(4) AILEx-MoEE
(e) AllEx + AT
Figure 3: Pscudo-labels (top row) and predictions (bottom row) after ten adaptation rounds. We visualize the sparse handcrafted pscudo-label (b) and the dense pseudo-label using the AdaptBN teacher (c), and the student predictions of AuEx-MoB trained with sparse (d) and dense (c) supervision. (a) shows the input left image.

---

Table 8: Ablation study on Pscudo Supervision. Average DI-all and EPE over 10 adaptation rounds on DrivingStereo. We use single- or dual-source supervision based on whether only one or both of handcrafted and learned signals are used.
All
dusky
cloudy
rainy
dusky
clondy
rainy
dusky
cloudy
rainy
Mean
Source
Method
EI- -DD-DD----EPPPPP -1l D1-
EEP.PPEDDDD--EEPPEPPP--EPPEEPPEE -PE EPE DI-al D1
D1-al
Single (a) proxy
4.01
1.01
2.4
0.84
4.44
1.13
2.54
0.86
2.23
0.83
3.89
1.2
2.72
0.88
2.29
0.84
3.89
1.22
2,98
0.97
photometric
1.04
2455
0.85
4.76
1.18
3.21
0.95
2.75
0.89
4.74
1.25
3.67
2.91
0.91
4.45
1.22
3.55
1,02
(c) AdaptBN
5.5
1.13
3.48
0.98
6.91
1.5
3.73
0.97
2.83
0.88
5.68
1.3
3.15
0.92
2.55
0.85
5.03
1.21
4.16
1.06
Dual proxy
+ (d) Source
4.28
1.03
2.4
0.84
4.62
1.18
2.75
0.88
2.3
0.82
4.0
1.13
2.78
0.87
2.31
082
3
1.1
3.09
0.95
FMA
4.27
1.03
2.38
0.84
4.56
1.16
2.84
0.88
2.26
0.82
4.09
116
3.11
2.32
0.82
4.03
1.15
3.16 0.96
+ (1) photometric
40
4.02
1.01
2,4
0.84
4.44
1.13
2.54
0.86
2.23
0.83
3.87
118
2.69
0.87
2.29
0.84
3.87
1.22
2,98
0,96
(g) AT (ours)
1.16
3.27
1.01
0.82
to the lack of neighboring pixels for reliable cost aggregation. As result, models relying solely on these handcrafted labels tend exhibit reduced adaptability in CTTA. In contrast, our dense pscudo-labels provide complete spatial coverage, allowing the model to adapt effectively even in unreliable regions. This confirms the role of dense, teacher-guided pseudo-supervision in enhancing spatial robustness under continual adaptation settings.
Pseudo-supervision ablation. Tab. 8 presents ablation study comparing various pseudo- supervision strategies using our AttEx-Mot based Parameter-efficient tuning method. Single-source supervision methods (a-c) perform worse overall. (a) Proxy supervises the model effectively, but limited guidance of sparse handcrafted pseudo-supervision means that the model's performance degradation later in the round. (b) photometric loss leads to increasingly higher error rates due to noisy guidance. (c) AdaptBN supervision alone shows more stable error reduction but underperforms when used without proxy supervision, the teacher model itself requires time adapt the target domain.
Dual-source variants (d-g) combine proxy labels with additional supervision to improve generalizati- ion across the entire input space. Although using fixed source model (d) enhances stability, it lacks adaptability under distribution shift. (e) EMA initially helps, but tends to propagate student errors. Following [46]. (f) augments proxy label with photometric and smoothing losses compensate for its sparsity, but does not yield meaningful improvement over (a) proxy-only. In contrast, our method (g) leverages a robust AdaptBN teacher to provide dense supervision, achieving the best overall results (D1-all 2.77% EPE 0.91), maintaining the performance in long-term and continuously changing conditions. These findings underscore the importance of dense, complementary supervision in overcoming the limitations of sparse pseudo-labels during continual adaptation.
C Additional Experiments Results
Table 9: Performance comparison of Continual Test-time Adaptation on DrivingStereo benchmark over 10 rounds for IGEV-Stereo and LightStereo backbone. Bold denotes best and AT denotes our method with dense pseudo-label D Deeeher-
Mean_ DrivingStereo
Backhone
Adapt.
(5)
EPF (px)
IGEV-Sterce
(a) no adapt.
(b) AdaptBN
3.87
0.98
(c) FT
3.56
0.96
(d) AnEEx-MoE
3.15
0.92
(c) AntEx-MoE AT
2.76
0.87
LightStereo
(a) adapt.
18.59
3.11
(b) AdagiiN
5.95
1.74
(c) FT
5.91
1.72
(d) AnEx-MoE
5.76
1.28
(D

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Table 10: Performance comparison of Continual Test-time Adaptation on DrivingStereo and KITTI RAW benchmark over rounds for MADNet2 backbone. Bold denotes best and AT denotes our method with dense pseudo-label
weahhrr
Mean..
DrivingStereo D1-all (%) EPE (px)
KITTI Di-ail (8)
Runtime (ms)
Backbone
Adapt.
EPE (px)
MADNe12 (a) no adapt.
10.44
1.70
3.92
1.09
(b) FT
6.13
1.73
1.13
0.85
(c) MAD++
5,86
1.41
1.24
0.88
18
AlEx-MMoE
(e) AuEx. MoE AT
4.83
1.09
1.10
0.85
49
As shown in Tab. 9 beyond the 3D cost aggregation model CoEx, we also evaluated our method on JGEV-Stereo 47
widely used iterative-refimmment backbone, and on LightStereo [48), a lightweight
real-time network with 2D cost aggregation. On all three architectures, RobIA with AttEx-MoE and Adache- Teacher consistently improves accuracy, demonstrating that our approach generalizes well to various stereo architectures.
Our components remain consistently effective when applied to IGEV-Stereo. Without adaptation, (a) IGEV-Source suffers from significant domain shift, yielding high D1-all errors 7.26% and EPE 1.32. While tuning only BN parameters ((b) AdaptBN) or full fine-tuning ((c) FULL++) reduces error, both approaches still struggle (Da-all (b) 3.87%, (c) 3.56%). In contrast, (d) our AttEx-MoE based PEFT lowers DI-all to 3.15%, and our RobIA implementation ((e) AuEx-MMB + AT) further improves it to 2.76% This demonstrates that our plug-and-play modules effectively enhance generalization top- hop-perorrming backbones.
Tab. .aaso shows that RoblA generalizes well to LightStereo. Without adaptation, (a) LightStereo- Source performs poorly due to severe domain shift (18.59%). (b) AdaptBN alone improves results to 5.95%, yet falls short dynamic scenes, Our AttEx-MoE module (e) further reduces the D1-all error to 5.76%, and RobIA (f) achieves the best performance (DI-all 4. 4.95%, EPE 1.11), confirming robustness and plug and-play nature of our method even compact backbones. These results with IGEV-Stereo and LightStereo demonstrate that RobIA is effective across both strong and lightweight backbones, and maintains robustness under significant domain shifts.
Furthermore, we re-implemented our method, including AtEEx-MoE and the AdaptBN teacher, on top of the MADNer2 [ encoder, which is also used by MAD++. Since MADNet2 lacks normalization layers, we inserted BatchNorm layers after each convolution to enable AdaptBN and maintain architectural consistency.
As shown in Table TO on KITTI, which presents relatively mild domain shifts, (b) FULL+ achieves DI-all 1.13% by updating all layers, while (d) our AutEx-MoE method achieves better result (1. (1.12%) with fewer parameters. (c) RoblA, which combines AutEx-MoE with the AdaptBN teacher, maintains a strong KITTI score of 1.1% 1.. On DrivingStereo, which exhibits stronger domain shifts, (b) FULL+ struggles with large domain shifts (6. 13%), and (c) MAD++ provides only minor improvement (5.86%). In contrast, (d) our AttEx-MoE method further reduces the error to 5.53%, and (e) RoblA achieves the best result at 4.83%. These results show that our anput-aware AttEx-Mol gate, combined with source supervision, matches full tuning on stable domains, and significantly outperforms both full and modular adaptation approaches under large distribution shifts.
Table 11: Performance comparison for Sequential Continual Test-time Adaptation across different datasets, from KITTI RAW (rounds 1-5) to DrivingSterco (rounds 6-10). Bold denotes best and AT denotes our method with dense pseudo-label D Dreacher-
KITTI Round
DrivingStereo (after KITTI)
ALL
Round
Backbone
Adapt.
D1-all (%)
EPE
D1-all (%)
EPE (px)
D1-ail (%)
EPE (px)
CoEx
AdaptBN
1.11
0.84
5.90
1.70
3.16
(b) AuEx-MoE
1.11
0.85
4.22
1.08
2.44
0.95
(c) AnEEx-MEE-
L.15
2.66
0.90
3

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Table 12: Performance comparison of Test-time Adaptation on KITTI RAW benchmarks. Bold denotes best and AT denotes our method with dense pseudo-label D Deaccer-
(8027 frames)
(28067 frames)
(1149 x frames)
(5674 frames)
Condition
City
Residential EPE
Campus (X2)
Road
Mean
Method
Adapt.
EPE
D1-all
EPE
RAFT-Sterco
(a) no adapt.
1.55
0.89
1.77
0.82
2.53
0.89
1.27
0.85
1.90
0.86
CREStereo 50
(b) no adapt.
1.77
0.99
1.71
0.89
3.21
1,07
2,00
0.89
2.29
0.96
IGEV Steren
47
(c) no adupt.
2.26
1.00
2.56
0.94
3.01
0.99
2.52
0.96
2.58
0.97
UniMatch
(d) no adapt.
2.66
1.13
3,20
1.10
3.10
1.13
2.26
1.08
2.81
1.11
MADNet
55
(c) no adapt.
4.04
1.10
4.05
1.03
6.07
1.29
4.01
1.08
4.54
1.13
(0) FT
1.23
0.90
1.05
0.80
2.39
0.92
1.02
0.83
1.42
0.86
(g) MAD++
1.39
0.93
1.16
0.83
2.88
1.00
1.14
0.85
1.64
0.90
ColFx 3
(b) no adapt.
2.66
1,07
2.66
0.99
3.65
1.11
2.46
0.98
2.86
1.04
() FT
0.83
0.76
1.49
0.8
0.75
0.79
0.96
0.80
FT AT
0.8
0.84
0,66
0.74
1,45
0.8
0.79
0.8
0.93
0.80
RobIA (ours)
(k) AnEx MoE.
0.99
0.87
0.96
0.78
1.6
0.82
0.81
1.11
0.82
() AtEEx-MoB AT
1.05
0.87
0.91
0.78
1.56
0.81
0.94
0.83
1.12
0.82
We simulated sequential CTTA scenario where the model first adapts to KITTI for 5 rounds, followed by adaptation to DrivingStereo for another 5 rounds, mimicking realistic progression from stable to a more challenging domain. Results are shown in Tab. [T] (b) and (c) show similar error rates with (a) on KITTI, but they achieve substantially lower error on DrivingStereo. (a), despite strong performance of 1.1 % on KITTI, fails to adapt in the second phase (5. (5.90%). These results highlight the robustness of our AuEx-MoE with AdaptBN teacher, which generalizes better under sequential, cross-domain conditions, mirroring realistic deployment settings.
TTA Experiments. Previous studies have demonstrated effective performance under TTA, which motivates us to assess whether our approach, designed for CTTA, also generalizes well in this setting. To evaluate the effectiveness of our method under standard test-time adaptation (TTA) settings, we conducted experiments on three real-world stereo datasets: KITTI RAW, DrivingStereo, and DSEC. The results reported in Tab. 12 Tab. 13 and Tab. 14 for KITTI RAW, DrivingStereo, and DSEC, respectively. Following prior work 45). we compared against MADNet2 [45), and several state-of- the-art stereo models known for strong generalization performance. The stereo models only trained on synthetic source datasets (a-d) exhibit significant performance degradation on target domains due to domain shifts. While adaptation- based methods generally improve accuracy, performance gains remain limited for efficiency-oriintte tfate-of-the-ant TTA methods (e-g).
Our efficient tuning method (k), which employs AttEx-MoE with input- feature excitation, achieves higher accuracy than prior TTA baselines, while remaining comparable to full model tuning and reducing computational cost, as discussed in the main paper. Results supervised by our dense pseudo-labels are shown in (j) and (l). Notably, substantial improvements by dense supervisions with AdaptBN Teacher are observed on DrivingStereo and DSEC, where pseudo-label sparsity is higher due to challenging conditions including adverse weather and nighttime imagery.
These findings demonstrate that our method is not only effective in the proposed continual adapta- tion scenario but also exhibits effective adaptability in standard TTA settings involving long-term adaptation within individual domains.
Qualitative Comparison of CTTA Results. We report qualitative comparisons of disparity maps predicted by various models and supervision strategies evaluated in our CTTA experiments. Figs. [nd]] examples from the cloudy and rainy sequences of the DrivingStereo dataset. For each example, predictions are visualized at round 5, and 10 to highlight temporal adaptation behaviors.
We observed that our method consistently improves predictions across rounds, particularly in chal- lenging regions such as reflective surfaces, low-texture areas, and image borders. Models trained with only sparse supervision show limited generalization beyond the confident regions of the handcrafted pseudo-labels, which limits their adaptability in less confident areas over time. In contrast, dense neeudo-supervision enables broader coverage and leads to stable improvements across the entire image, demonstrating stronger generalization under continual domain shifts.

---

Table 13: Performance comparison of Test-time Adaptation on DrivingStereo benchmarks. Bold denotes best and AT denotes our method with dense pseudo-label Deacher
(1667 frames)
(1119 frames)
(4950 frames)
Condition
dusky
cloudy
rainy D1-ail
Mean
Method
Adapt.
D1-all
EPE
D1-ail
EPE
EPE
DI-all
EPE
RAFT-Stereo EI
(a) adopt.
11.52
1.59
3.08
0.88
4.18
1.02
6.26
1.16
CREStereo 50
(b) no adupt.
17.43
3.61
7.08
1.07
9.53
1.97
IGEV-Stere
77
adapt.
11,70
1.85
3.57
0.95
5.27
1.26
6.95
1.35
UniMatch
5
(d) po adapt.
14.84
2.69
7.51
1.27
5.78
1.25
9.38
1.74
MADNer
+51
(c) adopt.
16.47
3.03
13,16
1.66
6.72
1.35
12.12
2.01
10.34
2.27
4.41
1.63
6.65
1.65
MAD++
10.06
2.01
5.25
1.09
4.34
1,09
6.55
1.40
adupt.
CoFx
FT
8.85
2.29
3.04
0.9
3.7
1.27
5.20
1.49
FT +
7.93
2.09
4.28
1.27
RobIA (ours)
(k) AllEx-MoEE MaE
9.05
2.28
3.21
0.96
0.91
5.02
1.38
ArE MalF
8.27
2.18
2.61
2.77
0.92
4.55
1.34
Table 14: Performance comparison of Test-time Adaptation DSEC benchmarks. Bold denotes best and AT denotes our method with dense pseudo-label D Deacher-
(883 Trames)
(1813 Trames)
(2315 frames)
(2405 Irames)
Condition
Night#1
Night82
Night83
Night84
Mean
Method
Adapt.
DI-all
EPE
DI-all
EPE
Di-all
EPE
DI-all
EPF
DI-all
EPF
RAFT-Siereo
adapt.
2.82
CREStereo S0
(b) no udapt.
11.34
2.38
23.48
3.19
15.37
2.39
12.42
1.75
15.65
2,43
IteeeStereo
adapt.
UniMarch
S
(d) adapt.
34.29
5.43
39.80
5.32
26.75
3.29
26.29
3.28
31.78
4.33
MADNet
adapt.
1.97
1.69
11.00
1.95
(i) FT
4.69
1.28
7.13
1.43
6,20
1.35
6.06
1.27
6.02
1.33
(g)
7.44
CoEx g+
(h) no adapt.
6.14
1.52
10:27
1.77
7.82
1.58
7.64
1.45
7.97
1.58
3.55
5.14
4.37
(j FT' AT
3.65
1.1
4.98
1.18
4.19
1.1
4.12
1.04
4.24
1.11
RobIA (ours)
(k) AnfEx-MoE
3,66
1.13
5.32
1.23
4.59
1.15
4.85
1.13
4.61
1.16
AnEx-MoB +
3.51
1.09
5.38
1.18
4.58
4.56
1.15
Round 5 Round
Round
Left
Cal
(elAnE
(DARE MAE
(ouIs)
Figure 4: Qualitative results for cloudy sequences in the DrivingStereo dataset.
Round Round
Round
(o) image
db) MADNe1
CoRe
Calls AT
(c) AnRRs-MR (ouns)
(0 ArFitMMRR (oun))
Figure 5: Qualitative results for rainy sequences the DrivingStereo dataset.
5

---

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Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
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Answer: [Yes]
Justification: We explicitly discuss the limitations of our method in the final paragraph of the Conclusion. In particular, note that AttEx-MoE relies predefined expert structures, which may limit flexibility in highly heterogeneous environments. We also outline potential future directions such incorporating uncertainty estimation or online expert refinement to improve long-term adaptability.
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Reviews:
[{"review_id": "TL8YeibVjv", "reviewer": "Reviewer_CJJ4", "summary": "The paper presents RobIA, a novel framework for Continual Test-time Adaptation (CTTA) in stereo depth estimation under dynamic domain shifts. The proposed method incorporates two key components: the Attend-and-Excite Mixture-of-Experts (AttEx-MoE) module for instance-aware adaptation and a Robust AdaptBN Teacher to enhance pseudo-label coverage. The authors show that RobIA outperforms existing methods in adapting to continually shifting domains, demonstrating strong performance in terms of both accuracy and computational efficiency. Overall, the approach is promising, and the experimental results are well-supported, though there are some areas for potential improvement.", "questions": "Please refer to weakness", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\nInnovation and Novelty: The introduction of AttEx-MoE and the dual-source pseudo-supervision strategy is a contribution to the field of continual adaptation for stereo depth estimation. The use of instance-aware, epipolar geometry-guided adaptation offers a unique approach to overcoming domain shifts in real-world settings.\n\nExperimental Evaluation: The authors conduct extensive experiments across several challenging benchmarks (KITTI RAW, DrivingStereo, and DSEC), showing that RobIA performs robustly in dynamic, real-world conditions. The results demonstrate superior adaptation performance when compared to existing methods like MADNet2 and CoEx.\n\nEfficient Use of Resources: RobIA achieves high performance while maintaining computational efficiency, which is essential for real-time applications such as autonomous driving. The proposed architecture leverages parameter-efficient modules, which helps in maintaining a balance between accuracy and resource usage.\n\n\nWeakness:\n1.In the KITTI15 leaderboard, among the top 15 models, 5 models with attached paper links include MonSter[1], DEFOM-Stereo[2], IGEV++ (DepthAny.)[3], StereoBase[4], and TC-Stereo[5]. MonSter[1], DEFOM-Stereo[2], and IGEV++ (DepthAny.)[3] utilize monocular Foundation models. IGEV[6] is a recently widely experimented baseline for stereo tasks, so the review recommends verifying the effectiveness of the method proposed in the paper by choosing one of IGEV or StereoBase. To the best knowledge of review, LightStereo[7] is the SOTA 2D-based model. The reviewer believes that the authors need to demonstrate the effectiveness of their method using at least one of the above methods. \n\n[1] Cheng, Junda, et al. \"MonSter: Marry Monodepth to Stereo Unleashes Power.\" (CVPR2025).\n\n[2] Jiang, Hualie, et al. \"DEFOM-Stereo: Depth Foundation Model Based Stereo Matching.\"  (CVPR2025).\n\n[3] G. Xu, X. Wang, Z. Zhang, J. Cheng, C. Liao and X. Yang: IGEV++: Iterative Multi-range Geometry Encoding Volumes for Stereo Matching. IEEE TPAMI 2025.\n\n[4] X. Guo, J. Lu, C. Zhang, Y. Wang, Y. Duan, T. Yang, Z. Zhu and L. Chen: OpenStereo: A Comprehensive Benchmark for Stereo Matching and Strong Baseline. arXiv preprint arXiv:2312.00343 2023.\n\n[5] J. Zeng, C. Yao, Y. Wu and Y. Jia: Temporally Consistent Stereo Matching. （ECCV2024).\n\n[6]  Xu, Gangwei, et al. \"Iterative geometry encoding volume for stereo matching.\" (CVPR2023).\n\n[7] \"Lightstereo: Channel boost is all you need for efficient 2d cost aggregation.\" (ICRA 2025).\n\n2. In Tables 2, 3 and 4, the best results need to be bolded to facilitate readers' reading.", "was_revised": true, "final_justification": "Thanks for the response. I am satisfied with the authors' rebuttal, so I maintain my opinion of accepting the paper. The authors are advised to refine the related work section in the final version.", "rebuttal": "We thank the reviewer **CJJ4** for their thoughtful feedback. We are encouraged that they found our idea innovative and novel, supported by extensive experiments and efficient use of limited resources.\n\n---\n\n## Weakness 1. Broader Backbone Evaluation\n\nWe appreciate the suggestion regarding evaluation using additional competitive architectures. In response, we extended our experiments to two widely recognized and publicly available backbones:\n\n* **IGEV-Stereo** [A]: a widely adopted iterative refinement model\n* **LightStereo** [B]: a lightweight model with 2D cost aggregation\n\n\n### 1-1. Evaluation on IGEV-Stereo\n\nAs shown in **Table A**, our method significantly improves performance. These results confirm that our plug-and-play modules effectively enhance generalization even on top-performing backbones.\n\n* (a) IGEV-Source suffers from domain shift (7.26\\% D1-all on DrivingStereo).\n* (b) AdaptBN tuning only BN parameters and (c) FULL++ still struggle for adaptation (3.87\\%, 3.56\\%, respectively).\n* (d) Our AttEx-MoE reduces DrivingStereo error to 3.15\\%.\n* (e) The proposed method, RobIA (MoE w/ AdaptBN-Teacher), achieves the best performance: 2.76\\% on DrivingStereo.\n\n\n#### Table A\n|             |              |                                     | Mean          |          | \n|-------------|--------------|-------------------------------------|---------------|----------|\n|             |              |                                     | DrivingStereo |          |\n| Backbone    |              | Adapt.                              | D1-all (%)    | EPE (px) | \n| IGEV-Stereo |              | (a) Source                          | 7.26          | 1.32     |\n|             |              | (b) AdaptBN                         | 3.87          | 0.98     | \n|             |              | (c) FULL++                          | 3.56          | 0.96     | \n|             | (Ours)       | (d) AttEx-MoE                       | 3.15          | 0.92     | \n|             | (Ours-RobIA) | (e) AttEx-MoE w/ AdaptBN-Teacher    | 2.76          | 0.87     |\n\n\n### 1-2. Evaluation on LightStereo\n\nRobIA also generalizes well to LightStereo, a compact real-time model. The results are shown in **Table B**.\n\n* (a) LightStereo-Source shows severe degradation under domain shift (18.59\\%).\n* (b) AdaptBN improves accuracy (5.95\\%) but remains limited on dynamic scenes.\n* (d) Our AdaptBN-Teacher improves CoEx FULL++ to 5.18\\%, reinforcing the strength of our dense pseudo-labeling strategy.\n* (e) AttEx-MoE alone reduces DrivingStereo error to 5.76\\%.\n* (f) The proposed method, RobIA, achieves the best results: 4.95\\% on DrivingStereo.\n\n\n#### Table B\n|             |              |                                     | Mean          |          | \n|-------------|--------------|-------------------------------------|---------------|----------|\n|             |              |                                     | DrivingStereo |          | \n| Backbone    |              | Adapt.                              | D1-all (%)    | EPE (px) |\n| LightStereo |              | (a) Source                          | 18.59         | 3.11     | \n|             |              | (b) AdaptBN                         | 5.95          | 1.74     | \n|             |              | (c) FULL++                          | 5.91          | 1.72     | \n|             |              | (d) FULL++ w/ AdaptBN-Teacher       | 5.18          | 1.13     |\n|             | (Ours)       | (e) AttEx-MoE                       | 5.76          | 1.28     | \n|             | (Ours-RobIA) | (f) AttEx-MoE w/ AdaptBN-Teacher    | 4.95          | 1.11     |\n\n---\n## Weakness 2. About Paper Formatting Concerns\n\nWe appreciate your careful reading and pointing out the formatting issues. We will correct the loss term and fix the boldface formatting in Table 1 and the other tables.\n\n## Citations\n* [A] Xu, Gangwei, et al. \"Iterative geometry encoding volume for stereo matching.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2023.\n* [B] Guo, Xianda, et al. \"LightStereo: Channel Boost Is All You Need for Efficient 2D Cost Aggregation.\" arXiv preprint arXiv:2406.19833 (2024)."}, {"review_id": "XjGbe2Vo9r", "reviewer": "Reviewer_i8yG", "summary": "This work studies the problem of tuning online a deep stereo network to unseen domains. The authors introduce two main novelties compared with previous works: the introduction of architecture modifications (mixture of experts) to make the model more robust to domain shifts and a new loss function using the prediction from a “teacher” model to speed up and stabilize convergence. The two modifications turned out to be effective across the different scenarios the author tested resulting in faster and more stable performance. The authors also introduce a new testing protocol by looping multiple times over the same video sequences. This setting should stress the ability of methods to adapt without catastrophic forgetting. The authors however do report performance also on the established way of testing test time trained stereo methods.", "questions": "* Are the timing in Tab. 3 taking into account the time spent generating pseudo labels using SGM or the AdaptBN teacher?\n\n* [weakness b] Can you comment on weakness b?", "limitations": "yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "## Strengths\n\n+ The test time training area is slightly under-explored in the depth estimation literature although it is one of the fields where it has proven to be most effective and practical. This work continues the existing exploration proposing both a new architecture which can be updated fast and a new loss that seems to be competitive and sometimes more effective than SOTA.\n\n+  The use of a mixture of experts (MoE) in CNN architecture is another under-explored area. I found the proposal from the authors interesting and somehow novel, although I’m not entirely convinced in calling it a MoE since (per my understanding) there is no sparsity enforced and all “experts” are often active at the same time. \n\n+ The work identifies one issue with existing SOTA around performance diverging for invalid regions and proposes an effective fix for it. The authors also designed  experiments to support their claim and solution (Fig. 2). \n\n## Weaknesses\n\na. **Computational cost**: If my understanding is correct, the proposed solution relies on keeping two versions of the same model (one w/AdaptBN training and one with MoE training) and performing 2 fwd pass and 2 bwd pass for each frame being processed. This results in a 10x latency increase compared to pure inference (Tab. 3 row (j) vs row (d)) which is significantly more than the previous SOTA (row (b) and (c) ) in Tab. 3.  \n\nb. **Missing motivation/implementation details**: From the current version of the manuscript it is not clear to me why the MoE architecture proposed should help generalization. The parameters of the mixture will anyway be trained on the source domain so doing inference with the mixture should in theory suffer from domain shifts as much as the full trained model. Where the mixture might help is in regularizing test time training avoiding modifying the architecture too much, though the motivation presented in the work are different. Can the author clarify whether there is anything special in the way the mixture is trained that would help it generalize better.\n\nC. [minor] **New evaluation protocol**: The authors propose a new evaluation protocol by looping over the same video sequence multiple times claiming that this scenario claiming to be the first method to look at test time adaptation for stereo under the \"continuous\" setting, while the previous methods operated under the assumption of a “single stationary target domain” (line 27-29). I would like to challenge this as the previous methods mentioned for SDE don’t have any assumption on the scenario considered and as such operate indeed on a continuous domain. Moreover a setting very similar to the one the authors considered is also reported in [1] Fig. 5 where the authors of [1] also test on looping over the same sequence. Challenging the motivation to develop a new test protocol would also imply that the methods should be compared using the old established protocol which the authors to their credits did in Tab. 9 Supplementary. In this table however the performance gains seem smaller with respect to the proposed new setting. This would benefit from some additional discussion in the document.\n\nD. [minor] **Possible missing comparison**: [2] uses a combination of proxy labels loss + smoothing + image reconstruction to compensate for the sparsity of the proxy labels. This is exactly the problem the authors are trying to solve with using an AdaptBn teacher. While [2] tests this solution offline there is no reason why it cannot be run online. A comparison would be interesting as it would save to the authors the cost of running the expensive AdaptBN teacher. \n\n## References \n\n1. [Tonioni, Alessio, et al. \"Real-time self-adaptive deep stereo.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019.](https://openaccess.thecvf.com/content_CVPR_2019/papers/Tonioni_Real-Time_Self-Adaptive_Deep_Stereo_CVPR_2019_paper.pdf)\n2. [Tonioni, Alessio, et al. \"Unsupervised domain adaptation for depth prediction from images.\" IEEE transactions on pattern analysis and machine intelligence 42.10 (2019): 2396-2409.](https://arxiv.org/pdf/1909.03943)", "was_revised": true, "final_justification": "Good work without major issue. The concern I raised in my initial review have been addressed. Overall I would suggest acceptance.", "rebuttal": "We appreciate the reviewer **i8yG** for highlighting the relevance of our test-time training setting, the novelty of our MoE-based design for CNNs, and our solution to performance degradation in invalid regions, along with the supporting experimental evidence.\n\n---\n## Weakness a. Computational Cost\n\nWe respectfully point out that comparing (j) MoE w/ AdaptBN-Teacher to (d) CoEx Source in Tab. 3 is may not be entirely fair, as (d) represents a pure inference baseline without any adaptation, which falls outside the scope of test-time adaptation. While (j) incurs higher latency, it achieves the best overall performance (D1-all 2.77\\%). More importantly, (i) our MoE-only variant strikes a favorable balance between performance and efficiency, achieving strong accuracy (D1-all 2.98\\%) with moderate runtime (97.34ms). Practitioners may choose between (i) and (j) depending on the desired trade-off.\n\n\n---\n\n## Weakness b. How does the MoE architecture help generalization?\n\nWe appreciate the reviewer’s question and the opportunity to clarify the motivation. AttEx-MoE improves generalization under domain shift by combining (1) geometry-aware, per-instance expert reweighting and (2) stable, soft channel modulation via Sigmoid gating, all within the PEFT setting. This design allows the model to adapt to instance-specific variations while preserving source knowledge through a frozen backbone.\n\nSpecifically, the row-wise self-attention router extracts informative epipolar-consistent context, enabling input-aware reweighting of channel experts without modifying the backbone.\nMore importantly, *Sigmoid-based gating* provides smooth and bounded scaling of expert activations on a per-instance basis. This is critical, as these gating values are directly multiplied with expert channels (See Eq. (2)).\nIn contrast to ReLU, which may cause hard-zeroing and unbounded scaling, Sigmoid gating ensures bounded and stable modulation, preserving gradient flow and improving robustness under distribution shift.\nThe effectiveness of Sigmoid gating function is empirically supported in Tab. 4, where it consistently outperforms ReLU across all router variants.\n\n### Summary\n* AttEx-MoE enhances generalization via input-dependent expert routing and stable Sigmoid-based gating.\n* The frozen backbone preserves source knowledge, while the AttEx-MoE dynamically adapts to instance-specific features without architectural changes.\n\n\n---\n\n## Weakness c. New Evaluation protocol\n\n### c-1. CTTA evaluation protocol\n\nWe acknowledge that Fig. 5 in [A] evaluates model performance in a looping scenario. However, it operates within a *single domain* (Alley 2 from Sintel [B]) where the target distribution is fixed. While useful for analyzing long-term adaptation, it does not assess adaptation under domain shift or knowledge preserving across diverse environments.\n\nIn contrast, our benchmark cycles through *multiple, distinct domains* (e.g., different weather conditions, nighttime scenes), introducing explicit distribution shifts. This allows us to evaluate continual adaptation challenges such as error accumulation and catastrophic forgetting, which fixed-domain setups cannot capture.\n\nWe will clarify this distinction and cite [A] in the revised manuscript.\n\n### c-2. TTA evaluation results\n\nAs noted by the reviewer, (j) RobIA shows smaller gains under the TTA benchmark (Tab. 9-11) compared to (i) CoEx-FULL++. This can happen as TTA operates within a single domain using longer sequences, favoring full-tuning methods with higher capacity.\n\nThis trend is also observed in MADNet 2, where (f) full tuning outperforms (g) modular update, particularly on KITTI ((f) 1.42\\%, (g) 1.64\\%) and DSEC ((f) 6.02\\%, (g) 7.44\\%), suggesting that high-capacity models benefit more from long-term adaptation in fixed-domain settings.\n\nNevertheless, (j) RobIA achieves comparable performance to full-tuned baselines (See Tab. 9, 11).\nInterestingly, in DrivingStereo (Tab. 10), where intra-domain variability is higher (e.g., dusky, cloudy, rainy), (j) RobIA (5.02\\%) outperforms (i) (5.2 \\%), indicating stronger robustness under dynamic conditions.\n\n\n---\n\n## Weakness d. Comparison with Tonioni, Alessio, et al. 2019\n\n[C] introduces two additional self-supervised losses---a smoothness loss $L_{smooth}$ and a photometric reconstruction loss $L_{photo}$---to compensate for sparse proxy labels via auxiliary supervision. This shares a similar motivation as our AdaptBN teacher.\n\nTo evaluate this strategy in our continual adaptation setting, we incorporated these loss terms into our framework as follows:\n$L$ = $L_{proxy}$ + $\\lambda$ $\\cdot$ $L_{smooth}$ + $\\lambda$ $\\cdot$ $L_{photo}$.\nIn **Table A**, (a) and (e) corresponds to the baseline using only the proxy loss. (c) and (f) apply the above strategy with $\\lambda=0.1$, but neither yields meaningful improvement over (a) and (d), respectively.\n\nIn contrast, (b) and (e)---our AdaptBN-Teacher loss---provides more reliable pseudo-labels, thus lowering the error ((a) 3.04\\% $\\rightarrow$ (b) 2.93\\%, (d) 2.98\\% $\\rightarrow$ (e) 2.77\\%).\nThis result confirms the effectiveness of our supervision strategy in continual adaptation.\n\nWe will include this ablation in the revised manuscript and clarify that our teacher strategy outperforms simple photometric augmentation.\n\n### Table A\n|                  |        |                                  | Mean   |      |\n|------------------|--------|----------------------------------|--------|------|\n| Backbone         | Source | Method                           | D1-all | EPE  |\n| CoEx             | Single | (a) $L_{proxy}$                  | 3.04   | 0.92 |\n|                  | Dual   | + (b) $L_{AdaptBN}$ (Ours)       | 2.93   | 0.91 |\n|                  |        | + (c) $L_{smooth}$ + $L_{photo}$ | 3.04   | 0.92 |\n| CoEx + AttEx-MoE | Single | (d) $L_{proxy}$                  | 2.98   | 0.97 |\n|                  | Dual   | + (e) $L_{AdaptBN}$ (Ours)       | 2.77   | 0.91 |\n|                  |        | + (f) $L_{smooth}$ + $L_{photo}$ | 2.98   | 0.96 |\n\n\n---\n## Question 1. Generation Time in Tab. 3\n\nIn the timing in Tab. 3, we included pseudo-label generation through AdaptBN teacher. However, we excluded SGM label generation time for the same comparison with existing TTA methods [D], which also used SGM as a proxy label but do not account for its generation time in their runtime analysis.\n\n---\n## About Paper Formatting Concerns\n\nWe appreciate your careful reading and pointing out the formatting issues. We will correct the loss term and fix the boldface formatting in Table 1 and the other tables.\n\n---\n\n## Citations\n* [A] Tonioni, Alessio, et al. \"Real-time self-adaptive deep stereo.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019.\n* [B] Butler, Daniel J., et al. \"A naturalistic open source movie for optical flow evaluation.\" European conference on computer vision. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.\n* [C] Tonioni, Alessio, et al. \"Unsupervised domain adaptation for depth prediction from images.\" IEEE transactions on pattern analysis and machine intelligence 42.10 (2019): 2396-2409.\n* [D] Poggi, Matteo, et al. \"Continual adaptation for deep stereo.\" IEEE Transactions on Pattern Analysis and Machine Intelligence 44.9 (2021): 4713-4729."}, {"review_id": "seZzzQiaEg", "reviewer": "Reviewer_4p4z", "summary": "This paper proposes **RobIA**, a novel Robust, Instance-Aware framework for Continual Test-Time Adaptation (CTTA) in stereo depth estimation. RobIA integrates two key components: (1) Attend-and-Excite Mixture-of-Experts (AttEx-MoE), a parameter-efficient module that dynamically routes input to frozen experts via lightweight self-attention mechanism tailored to epipolar geometry, and (2) Robust AdaptBN Teacher, a PEFT-based teacher model that provides dense pseudo-supervision by complementing sparse handcrafted labels. Extensive experiments demonstrate that RobIA achieves superior adaptation performance across dynamic target domains while maintaining computational efficiency.", "questions": "Please refer to the Weaknesses section. I will adjust my rating according to the author's response.", "limitations": "The authors have briefly discussed the limitations.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "### **Strengths**\n\n1. RobIA presents a simple and efficient framework for real-time continual test-time adaptation (CTTA) in stereo depth estimation, addressing an important challenge for reliable real-world applications.\n2. The author proposes a new benchmark from existing TTA sequences that feature frequent domain shifts. This benchmark more accurately reflects real-world conditions, where environmental changes occur regularly and previously encountered scenarios may reappear. It offers a more appropriate criterion for evaluating CTTA frameworks.\n3. The paper is well-organized and easy to follow. Each component of the proposed framework is well-motivated and clearly explained.\n\n### **Weaknesses**\n\n1. **Lack of direct comparison with key baseline.** RobIA builds upon CoEx [1] stereo network, but the Modular ADaptation (MAD++) method from MADNet2 [2] is only evaluated on top of the original MADNet2 network. Comparing RobIA with MAD++ in this setting appears unfair. Furthermore, MAD++ with MADNet2 runs significantly faster than RobIA with the AdaptBN teacher (18ms vs. 204ms as reported in Table 3). A fairer comparison under similar runtime constraints (*e.g.*, by selecting additional modules in MAD++) would better clarify whether RobIA offers substantial improvements over the Modular ADaptation approach.\n2. **Limited overall performance gain.** According to Table 1, 2 and 8 (in the supplementary material), the performance improvements with RobIA seem marginal compared to the AdaptBN approach with minimal trainable parameters (0.03M compared to 1.22M). Notably, as shown in Table 8, CoEx with AdaptBN even outperforms RobIA (w/ AdaptBN teacher) in KITTI RAW CTTA benchmark by a noticeable margin. This raises concerns about the effectiveness of RobIA.\n3. **Minor presentation issues and typos.** I would suggest the author use `w/ Teacher` instead of `w/ AdaptBN` to reduce ambiguity with tunning batch normalization layers; In line 242, the loss should be $\\mathcal{L}_{\\text{teacher}}$ in the third equation; Bold and Underline fonts are missing from Tabel 2 onward.\n\n\n\n[1] Bangunharcana, Antyanta, et al. \"Correlate-and-excite: Real-time stereo matching via guided cost volume excitation.\" *2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)*. IEEE, 2021.\n\n[2] Poggi, Matteo, et al. \"Continual adaptation for deep stereo.\" *IEEE Transactions on Pattern Analysis and Machine Intelligence* 44.9 (2021): 4713-4729.", "was_revised": true, "final_justification": "The authors addressed my major concerns during rebuttal.\n\nGiven that there is still room for improvements in performance, latency, and computational costs compared to the baselines, I would suggest the rating of Borderline Accept after rebuttal.", "rebuttal": "We appreciate the reviewer **4p4z** for recognizing the simplicity and efficiency of our CTTA framework, the realism of our benchmark design, and the clarity of our overall presentation.\n\n\n---\n\n## Weakness 1. Comparison with MAD++\n\nWe thank the reviewer for highlighting the importance of a fair comparison with MAD++. To address this, we re-implemented our method, including AttEx-MoE and the AdaptBN teacher, on top of the MADNet2 [A] encoder, which is also used by MAD++ in Table 1, 2, and 8. Since MADNet2 lacks normalization layers, we inserted BatchNorm layers after each convolution to enable AdaptBN and maintain architectural consistency.\n\n**Table A** presents CTTA results using the MADNet2 backbone along with respective runtime, as requested.\n\n* On KITTI, which presents relatively mild domain shifts, (b) FULL++ achieves D1-all 1.13\\% by updating all layers, while (d) our AttEx-MoE method achieves a better result (1.12\\%) with fewer parameters. (e) RobIA, which combines AttEx-MoE with the AdaptBN teacher, maintains a strong KITTI score of 1.1\\%.\n\n* On DrivingStereo, which exhibits stronger domain shifts, (b) FULL++ struggles with large domain shifts (6.13\\%), and (c) MAD++ provides only minor improvement (5.86\\%). In contrast, (d) our AttEx-MoE method further reduces the error to 5.53\\%, and (e) RobIA achieves the best result at 4.83\\%.\n\nThese results show that our input-aware AttEx-MoE, combined with dual-source supervision, matches full tuning on stable domains (KITTI), and significantly outperforms both full and modular adaptation approaches under large distribution shifts (DrivingStereo).\n\n### Table A\n|          |              |                                     | Mean          |          |            |          |         |\n|----------|--------------|-------------------------------------|---------------|----------|------------|----------|---------|\n|          |              |                                     | DrivingStereo |          | KITTI      |          | Runtime |\n| Backbone |              | Adapt.                              | D1-all (%)    | EPE (px) | D1-all (%) | EPE (px) | (ms)    |\n| MADNet2  |              | (a) Source                          | 10.44         | 1.7      | 3.92       | 1.09     | 11      |\n|          |              | (b) FULL++                          | 6.13          | 1.73     | 1.13       | 0.85     | 31      |\n|          |              | (c) MAD++                           | 5.86          | 1.41     | 1.24       | 0.88     | 18      |\n|          | (Ours)       | (d) AttEx-MoE                       | 5.53          | 1.20     | 1.12       | 0.86     | 23      |\n|          | (Ours-RobIA) | (e) AttEx-MoE w/ AdaptBN-Teacher    | 4.83          | 1.09     | 1.10       | 0.85     | 49      |\n\n\n---\n\n## Weakness 2. Limited overall performance gain\n\n### **RobIA’s Gains Are Significant Under Realistic Domain Shifts.**\n\nWe appreciate the concern regarding the seemingly marginal improvements of RobIA on KITTI. We agree that the performance gap may appear small. However, we would like to emphasize that this is primarily due to mild domain shifts and dense SGM pseudo-labels on KITTI, which make it easier to adapt even with lightweight methods like AdaptBN. In contrast, under more realistic and challenging conditions such as weather variation (DrivingStereo) and nighttime scenes (DSEC), RobIA demonstrates substantial improvements over AdaptBN.\n\n* In Tab. 1 and 2, (e) AdaptBN (0.03M trainable parameters) struggles under substantial domain shifts such as weather changes (DrivingStereo) and nighttime scenes (DSEC), yielding D1-all scores of 3.08\\% and 4.54\\%, respectively.\n\n* In contrast, (h) AttEx-MoE w/ AdaptBN-Teacher (1.22M) significantly improves performance to 2.77\\% and 4.46\\%.\n\n\n### **KITTI Performance Reflects Domain Simplicity, Not Method Weakness.**\n\nAdaptBN performs competitively on KITTI (0.91\\%, Tab.8), likely due to its (1) smaller domain gap (e.g., city $\\rightarrow$ residential) and (2) higher SGM pseudo-label density (92\\%) compared to DrivingStereo (72\\%) and DSEC (45\\%). These factors suggest that KITTI presents a more favorable adaptation setting, where even lightweight methods like AdaptBN can perform well.\n\n### **Sequential Adaptation, KITTI $\\rightarrow$ DrivingStereo, Further Highlights RobIA's Advantage.**\n\nTo further support this point, we simulated a sequential CTTA scenario where the model first adapts to KITTI for 5 rounds, followed by adaptation to DrivingStereo for another 5 rounds, mimicking a realistic progression from a stable to a more challenging domain. Results are shown in **Table B**.\n\n* (b) and (c) show similar error rates with (a) on KITTI, but they achieve substantially lower error on DrivingStereo.\n\n* (a), despite strong performance of 1.11\\% on KITTI, fails to adapt in the second phase (5.90\\%).\n\nThese results highlight the robustness of our AttEx-MoE design, which generalizes better under sequential, cross-domain conditions, mirroring realistic deployment settings.\n\n\n### Table B\n|        |              |                                  | KITTI                   |          | DrivingStereo (after KITTI) |          | ALL        |          |\n|--------|--------------|----------------------------------|-------------------------|----------|-----------------------------|----------|------------|----------|\n|        |              |                                  | Round 1 $\\rightarrow$ 5 |          | Round 6 $\\rightarrow$ 10    |          |            |          |\n| Method |              | Adapt.                           | D1-all (%)              | EPE (px) | D1-all (%)                  | EPE (px) | D1-all (%) | EPE (px) |\n| CoEx   |              | (a) AdaptBN                      | 1.11                    | 0.84     | 5.9                         | 1.7      | 3.16       | 1.21     |\n|        | (Ours)       | (b) AttEx-MoE                    | 1.11                    | 0.85     | 4.22                        | 1.08     | 2.44       | 0.95     |\n|        | (Ours-RobIA) | (c) AttEx-MoE w/ AdaptBN-Teacher | 1.15                    | 0.84     | 2.66                        | 0.9      | 1.8        | 0.87     |\n\n\n---\n## Weakness 3. About Paper Formatting Concerns\n\nWe appreciate your careful reading and pointing out the formatting issues. We will correct the loss term and fix the boldface formatting in Table 1 and the other tables. Additionally, we will rename the 'w/ AdaptBN' label to avoid ambiguity.\n\n---\n\n## Citations\n* [A] Tonioni, Alessio, et al. \"Real-time self-adaptive deep stereo.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019."}, {"review_id": "9fJzDtDoXA", "reviewer": "Reviewer_VsX9", "summary": "The article introduces a novel method for Continual Test-Time Adaptation in Deep Stereo. The paper's main contributions are: a new Mixture of Experts-based module for convolutional networks, and a new strategy for generating pseudo-labels during training.\nRegarding the first contribution, the proposed module uses row-wise self-attention to extract an embedding that is fed into a gating network. This approach is both efficient and preserves some spatial structure from the input images. The resulting embedding is used to produce channel-wise modulations for each training instance, enabling greater model flexibility. Moreover, using a sigmoid activation function instead of ReLU helps retain information across all channels of the base model, which is particularly beneficial in scenarios where the backbone is frozen.\nAs for the second contribution, the method proposes generating dense pseudo-labels by combining sparse pseudo-labels obtained through traditional methods (e.g., SGM) with dense pseudo-labels generated by a teacher network for the remaining regions. Specifically, the teacher network is implemented using an AdaptBN version of the base model, rather than a conventional EMA approach. This choice is motivated by the intuition that AdaptBN is less prone to reinforcing the errors of hand-crafted pseudo-labels, thus providing a more reliable supervision signal.", "questions": "See Weaknesses section above.", "limitations": "Some limitations are presented in the paper.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "__Strength__\n\n- The paper is well-written, well-structured, and well-curated. \n\n- The various ideas have been thoroughly motivated, both logically and experimentally. \n\n- The experiments are rigorously conducted and also well-described in the paper.\n\n__Weaknesses__\n\nIn order of importance:\n\nA. Some stereo networks have architectural differences compared to CoeX. For instance, could the proposed method be applied to frameworks such as HITNet [a] or TemporalStereo [b]?\n\n[a] Tankovich et al. HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching. CVPR 2021.\n\n[b] Zhang et al. TemporalStereo - Efficient Spatial-Temporal Stereo Matching Network. IROS 2023\n\nB. The AttEX-MoE strategy appears to be particularly tailored to CNN-based networks. However, many recent stereo architectures rely on transformer-based approaches. Is the proposed method applicable to these types of architectures as well?\n\nC. The pseudo-label generation mechanism using AdaptBN implicitly assumes that the network employs Batch Normalization (BN) layers. How could this technique be extended to scenarios where the base network uses a different type of normalization?\n\nD. While row-wise self-attentions improve computational efficiency, the emphasis on epipolar lines is unclear. If leveraging epipolar geometry to estimate embeddings is a key factor for performance, this should be validated experimentally. For example, one possible approach could be to compare it with a strategy that does not exploit epipolar geometry but still partially preserves spatial structure and offers similar efficiency (e.g., using column-wise instead of row-wise embeddings).\n\n\n__Minor Weaknesses__\n\na.\tMissing bolds and underlines to highlight the best results in Tables 2, 3, and 4\n\nb.\tSeveral highly relevant results are only presented in the appendix (e.g., Tables 8, 9, and 10). While it's understandable that these results were moved to the appendix due to space constraints, they should at least be referenced or briefly discussed in the main paper to emphasize their importance.\n\n__Overall Comments__\n\nThe paper is well-organized and structured. It presents novel ideas that are well-motivated and interesting. The main limitation of the work lies in its specificity to a particular architecture, relying on specific normalization layers and a convolution-based design. A better understanding of this limitation could lead me to provide a more grounded result. It would also be interesting to investigate whether the emphasis on epipolar geometry is necessary for the AttEX-MoE component.", "was_revised": true, "final_justification": "After carefully reviewing the rebuttal, some of my concerns, particularly points B, C, and D, remain only partially addressed. However, given the authors’ commitment to expand on these aspects in the camera-ready version, and considering the overall strength of the paper, I recommend acceptance.", "rebuttal": "We appreciate the reviewer **VsX9** for recognizing the significance of our CTTA framework for stereo depth estimation, the motivation behind our MoE and AdaptBN-based components, and the clarity and thoroughness of the manuscript and experiments.\n\n\n---\n\n## Weakness A. Applicability to Other Architectures\n\nYes, our method is applicable to frameworks such as HITNet [A] and TemporalStereo [B], as both AttEx-MoE and AdaptBN-Teacher are designed as plug-and-play modules compatible with a wide range of CNN-based stereo architectures.\n\nAttEx-MoE applies instance-aware, channel-wise excitation exclusively within the feature extractor, as this stage is critical for capturing high-quality, geometry-aware features for stereo matching.\nIts row-wise self-attention router operates along epipolar lines and naturally aligns with the geometry of rectified stereo images, making it broadly applicable to architectures that process stereo pairs. We elaborate further on this benefit in the **Weakness D. Why Epipolar Line?** section.\n\nSimilarly, AdaptBN is applicable to any CNN networks using batch normalization. We provide further discussion in the **Weakness C. Substitutes for Batch Normalization** section.\n\n\n---\n\n## Weakness B. Applicability to Transformer-based Architectures\n\nYes, AttEx-MoE is compatible with Transformer-based stereo architectures.\n\nWhile our experiments focus on CNN backbones (e.g., CoEx), AttEx-MoE is backbone-agnostic. Its components, the row-wise self-attention router and Sigmoid gating, operate on feature maps, and can be applied to any encoder as long as it maintains image's spatial feature structure.\n\nIn Transformer-based stereo models [C] [D], token sequences can be reshaped into feature maps, enabling seamless integration of AttEx-MoE into the encoder without architectural changes.\n\n\n---\n\n## Weakness C. Substitutes for Batch Normalization\n\nAdaptBN is not inherently tied to BatchNorm but is based on adapting a small set of affine parameters within a frozen backbone. While we do not experiment with other normalization schemes in this work, the underlying principle is conceptually generalizable:\n\n* **GroupNorm / InstanceNorm**: Like BN, both provide per-channel affine parameters $(\\gamma, \\beta)$, suggesting that similar adaptation (*e.g.*, AdaptGN or AdaptIN) could be feasible.\n\n* **LayerNorm (e.g., in ViT)**: Despite operating over hidden dimensions, its affine parameters $(\\gamma_{LN}, \\beta_{LN})$ may also be adapted per layer to enable token-level adaptation.\n\nFor backbones without explicit affine layers (*e.g.*, Conv + ReLU), a lightweight 1×1 'scale-shift' adapter (two parameters per channel) can be inserted with minimal overhead.\n\nIn summary, AdaptBN’s effectiveness stems from adapting affine modulation parameters, not BatchNorm specifically. This approach may be extended to other architectures with compatible parameterizations. We will clarify this in the revised manuscript.\n\n\n---\n\n## Weakness D. Why Epipolar Line?\n\nWhile full self-attention is effective for modeling global dependencies, it incurs prohibitive computational cost. In rectified stereo geometry, corresponding points across view lie along the same image row, making horizontal context more relevant than vertical context for disparity estimation [C].\nWe thereby adopt row-wise self-attention, which focuses along epipolar-consistent scanlines. This preserves disparity-relevant context while reducing the attention complexity from $H \\times W \\times H \\times W$ to $H \\times W \\times W$, lowering memory and FLOPs by a factor of $H$.\nThus, row-wise attention is not merely a speed heuristic, but a geometry-aware inductive bias that enhances both efficiency and performance.\nEmpirical results in Tab. 4 support this design: among various router alternatives, row-wise attention achieves the best performance.\n\n---\n## About Paper Formatting Concerns\n\nWe appreciate your careful reading and pointing out the formatting issues. We will fix the boldface formatting in Table 1 and the other tables.\n\n---\n\n## Citations\n* [A] Tankovich, Vladimir, et al. \"Hitnet: Hierarchical iterative tile refinement network for real-time stereo matching.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021.\n* [B] Zhang, Youmin, Matteo Poggi, and Stefano Mattoccia. \"Temporalstereo: Efficient spatial-temporal stereo matching network.\" 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2023.\n* [C] Li, Zhaoshuo, et al. \"Revisiting stereo depth estimation from a sequence-to-sequence perspective with transformers.\" Proceedings of the IEEE/CVF international conference on computer vision. 2021.\n* [D] Weinzaepfel, Philippe, et al. \"Croco v2: Improved cross-view completion pre-training for stereo matching and optical flow.\" Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023."}]

Author rebuttal:

Meta-review: This paper proposes a continual Test-time Adaptation in stereo depth estimation, with two key components, two key components: the Attend-and-Excite Mixture-of-Experts (AttEx-MoE) module for instance-aware adaptation and a Robust AdaptBN Teacher to enhance pseudo-labels. After the rebuttal, the reviewers were generally positive to the paper’s contribution. Their raised major concerns regarding unclarified motivation/complexity analysis/fairness in comparison, generalization to other methods and architectures, required additional comparisons, were satisfactory addressed in the rebuttal, as acknowledged by all the reviewers. After reading the paper, the comments and the rebuttal, the AC agreed with the reviewers’ consensus that the paper could be accepted. The AC reminds the authors to incorporate the committed revisions in the rebuttal to the final version.
Decision: Accept (poster)