RobustSpring: Benchmarking Robustness to Image Corruptions for Optical Flow, Scene Flow and Stereo
Authors: Victor Oei
Jenny Schmalfuss
Lukas Mehl
Madlen Bartsch
Shashank Agnihotri
Margret Keuper
Andreas Bulling
Andres Bruhn
Venue: ICLR 2026 Poster (2026)
Keywords: out-of-distribution
image corruptions
optical flow
scene flow
stereo
Primary area: datasets and benchmarks

Abstract: Standard benchmarks for optical flow, scene flow, and stereo vision algorithms generally focus on model accuracy rather than robustness to image corruptions like noise or rain. Hence, the resilience of models to such real-world perturbations is largely unquantified. To address this, we present RobustSpring, a comprehensive dataset and benchmark for evaluating robustness to image corruptions for optical flow, scene flow, and stereo models. RobustSpring applies 20 different image corruptions, including noise, blur, color changes, quality degradations, and weather distortions, in a time-, stereo-, and depth-consistent manner to the high-resolution Spring dataset, creating a suite of 20,000 corrupted images that reflect challenging conditions. RobustSpring enables comparisons of model robustness via a new corruption robustness metric. Integration with the Spring benchmark enables public two-axis evaluations of both accuracy and robustness. We benchmark a curated selection of initial models, observing that robustness varies widely by corruption type and experimentally show that evaluations on RobustSpring indicate real-world robustness. RobustSpring is a new computer vision benchmark that treats robustness as a first-class citizen to foster models that combine accuracy with resilience.

Published as a conference paper at ICLR 2026
ROBUSTSPRING: BENCHMARKING ROBUSTNESS TO IMAGE CORRUPTIONS FOR OPTICAL FLOW, SCENE FLOW AND STEREO
Victor Oci
Jenny Schmalfuss" 1 1.2 Margret Keuper
Lukas Mehl
Madlen Bartsch Andrés Bruhn'
Shashank Agnihotri
Andreas Bulling"
University of Stuttgart,
NVIDIA,
University of Mannheim,
MPI for Informatics
Blat
Color
Netic
Quality
Wenther
RebindSpring
S
Blightw
Phonlap
Swite
Opsial now
JPEG
DV
Shano
Scone flow
Sprackic
Elo Phasker
Stereo
Moaes
RobustSpring is a novel image corruption benchmark for optical flow, scene flow, and stereo. It evaluates 20 image corruptions including blurs, color changes, noises, quality degrada- tions, and weather, applied to stereo video data from Spring. For comprehensive robustness evalua- tions, RobustSpring' image corruptions are integrated time, stereo, and depth, where applicable.
Figure 1:
ABSTRACT
Standard benchmarks for optical flow, scene flow, and stereo vision algorithms generally focus model accuracy rather than robustness to image corruptions like noise or rain. Hence, the resilience of models to such real-world perturba- tions largely unquantified. To address this, we present RobustSpring, com- prehensive dataset and benchmark for evaluating robustness to image corruptions for optical flow, scene flow, and stereo models. RobustSpring applies 20 different image corruptions, including noise, blur, color changes, quality degradations, and weather distortions, in time-, stereo-, and depth-consistent manner to the high- resolution Spring dataset, creating a suite of 20,000 corrupted images that reflect challenging conditions. RobustSpring enables comparisons of model robustness via new corruption robustness metric. Integration with the Spring benchmark enables two-axis evaluations of both accuracy and robustness. We benchmark a curated selection of initial models, observing that robustness varies widely by cor- ruption type, and experimentally show that evaluations on RobustSpring indicate real-world robustness. RobustSpring is new computer vision benchmark to treat robustness as a first-class citizen, fostering models that are accurate and resilient.
INTRODUCTION
Optical flow, scene flow, and stereo vision algorithms estimate dense correspondences and enable real-world applications like robot navigation (McGuire et al. 2017 Zhang et al., [025 Lambert
al.
2024). video processing (Mehl al. 2024), structure- conttuure-rro--oooee from-motion (Maurer et al.] 2018] Phan
et al.
2020), medical image registration (Mocanu et al. 2021), or surgical assistance (Rosa et al.
Equal contribution. Correspondence to victor, oeievis uniisstutgaar.... Work done the University of Stuttgart, Jenny Schmalfuss is currently affiliated with NVIDIA.

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Published as a conference paper at ICLR 2026
Philipp et al. 2022). While estimation quality continuously improves on accuracy- -rivven benchmarks (Mehl et al. 2023b; Menze & Geiger 2015 Butler et al. 2012 Baker et al., 2011 Scharstein et al. [014 Geiger et al. 2012 Richter et al. 201 Schhps et al 2017). their robustness
2019
to real-world visual corruptions like noise or compression artifacts is rarely systematically assessed. This lack of systematic assessment is problematic, as better accuracy does not necessarily trans- late to improved robustness, and can even harm model robustness (Tsipras et al. [019 [Schmalfuss ct al., 2022b). Though image data in KITTI (Menze & Geiger 2015), Sintel (Buterr et al. 2012), or Spring (Mehl et al. 2023b) comes with degradations like motion blurs, depth-of-field or bright- ness changes, they result from real-world data capture or efforts to increase data realism, but were not included to systematically study model predictions under image corruptions. Broad corruption- robustness studies as they exist for image classification (Hendrycks & Dietterich, 2019 Müller et al.) 2023), 3D object detection (Michaelis et al., 2019 Kong et al., (023) or monocular arr depth estima- tion (Kar et al. 2022) are rare for conss-correspoddeec tasks, where studies are limited to specific degradations like weather (Schmalfuss et al. (023) or low-light (Zheng et al. (020). This not only leaves uncertainty about the reliability of dense matching algorithms in real-world scenarios. also prevents systematic efforts to improve their robustness.
To enable systematic studies on the image corruption robustness of optical flow, scene flow, and stereo, we propose the RobustSpring dataset. Based on Spring (Mehl al. 2023b), it jointly bench- marks robustness of all three tasks on corrupted stereo videos. While prior image corruptions affect the monocular 2D or 3D space (Hendrycks Dietterich 2019 Michaelis al.) 2019. Kar et al. 2022 Schmalfuss et al. (025),, RobustSpring's 's image corruptions are integrated in time, stereo, and depth and thus tailored dense matching tasks. A principled corruption robustness metric and an accompanying benchmark framework make RobustSpring the first systematic tool to evaluate and improve dense matching robustness to image corruptions.
Contributions. Figure
! gives an overview of RobustSpring, In summary, we make the following
contributions:
(1) Tailored image corruptions. RobustSpring is the first image corruption dataset for optical flow, scene flow, and stereo. integrates 20 corruptions for blurs, noises, tints, artifacts, and weather in time, stereo, and depth.
(2) Corruption robustness metric. We propose corruption robustness metric based on Lips- chitz continuity, which subsamples the clean clean-corrupted prediction difference and disentan- gles robustness and accuracy.
(3) Benchmark functionality. RobustSpring's standardized evaluation enables community- driven robustness comparisons of dense matching models. Public robustness benchmarking is integrated with Spring's 's website (hitps://sprigg-brt ben.hnnnknnccch..orr
(4) Initial robustness evaluation. We benchmark nine optical flow, two scene flow, and six stereo models. All models are corruption sensitive, which reveals concealed robustness deficits on dense matching models.
Intended Use. RobustSpring is not a fine-tuning dataset, but a benchmark of how dense matching models generalize to unseen image corruptions. It seeks to foster robustness research and, simulta- neously, helps assess real-world applicability of models. Hence, it is essential to tie RobustSpring to an existing accuracy benchmark like Spring, as this minimizes the robustness evaluation hurdle for researchers. While RobustSpring treats corruptions interpretation depends on the application domain. For instance, in autonomous driving, rain or snow are typically considered disturbances to be ignored for stable navigation, whereas in video editing or cinematic rendering, they may constitute meaningful scene content. To accommodate such differ- ences, RobustSpring provides results per corruption type, allowing end-users to focus only on those corruptions that align with their intended application.
perturbations to assess robustness, their
2 RELATED WORK
While the quality of optical flow, scene flow, and stereo models advanced for over three decades, their robustness recently regained attention result of brittle deep learning generalization (Ranjan ct al. 2019, Schmalfuss et al. 2022b). We review robustness in dense matching, particularly image corruptions and metrics.
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Robustness in Dense Matching. Robustness research for optical flow, scene flow, and stereo models often focuses on adversarial attacks, which quantify prediction errors for optimized image pertur- bations. Most attacks are for optical flow (Agnihotri et al.] 2024c
Schmalfuss et al. [2023 2022b
Schrodi et al. 2022 Ranjan et al.] 2019; Yamanaka et al., 2021 Koren et al. (022) rather than
stereo (Berger et al. 2022 Wong et al. (022)) and scene flow (Wagg al. 2222a] Mahima al.
2025). As remedies to adversarial vulnerability (Agnihotri et al. 2024b al 2023]
/ Schrodi et al. 2022
Anand et al. 2020) may be overcome through specialized optimization (Scheurer et al. 2024), an- other line of robustness research considers non-adversarial data shifts. Those come in two flavors: generalization across datasets, i.e. the Robust Vision Challenge http://www.robbustiiiioon.m
or
effective robustness (Bauer et al. 2025), and robusiness to image corruptions. Work on dense match-
ing models typically reports generalization (Mehi et al. 2023a; TTed & Deng 2020; 2021
Lipson
202T Huang et al., 2022 Xu et al. 2022b) to several datasets, which span synthetic Meeh ctt al.,
et al.
2023b; Butler et al., 2012; Richter et al. [2017: Mayer et al. 2016 Dosovitskiy et al.
2015
Gaidon et al. 2016 Ranjan et al.) [2020] Li al. 2024)) and real world data (Geiger et al.
2012
Menze & Geiger 2015] Kondermann et al. 2016]] |Scharstein et al. 2014 Schöps et al.) 20017]
often
in automotive contexts. While some datasets contain image corruptions, e.g. motion blur, depth of field, fog, noise, or brightness changes (Sun et al.) 2021 Butler et al.
2012; Mehl et al.) 2023b
Menze & Geiger 2015), they do not systematically assess corruption robustness. Yet, in the wild, robustness to image corruptions is crucial. For optical flow, systematic low light (Zheng et al.
2020
and weather datasets (Schmalfuss et al. 2022a 2023) exist, and|Schrood et al. (2022); Yi al. (024) apply 2D image corruptions (Hendrycks & Dietterich 2019) to optical flow data. Beyond these iso- lated works on optical flow, no systematic image-corruption study before RobustSpring spans all three dense matching tasks and includes scene flow or stereo.
Robustness to Image Corruptions. Popularized by 2D common corruptions (Hendrycks & Dietterich] 2019). the field of image corruption robustness rapidly expanded from classifica- tion (Hendrycks & Dietterich] [2019] Müller al. (2023) to depth estimation (Kar et al.. 2022). 3D object detection (Michaelis et al.] 2019] Kong et al. 2023), , and semantic segmentation ( (Kong et al.
2023). Conceptually. corruptions were extended to the 3D space (Kar et al.) 2022), LiDAR (Kong et al. 2023). and procedural rendering (Drenkow & Unberath] (024), but none have been tailored to the depth-, stereo-, and time-dependent setup of dense matching with optical flow, scene flow, and stereo.
Robustness Metrics and Benchmarks. Most robustness metrics for dense matching differ by whether they utilize ground truth (Ranjan et al. 2019 Agnihouri et al. 2024c] Yi et al. 2024) or
not (Schmalfuss al. 2022b 2023 2022a). However, multiple works (Schmalfuss al. 2022bb
Tsipras et al. 2019; Taori et al. 2020) show that robustness and accuracy are competing qualities that should not be quantified together. This informs our robustness metric. RobustSpring is the first
matehig robustness benchmark, and joins prior classification robustness benchmarks (Croce
et al. 2021 Jung al. 2023 Tang et al. (02T)
ROBUST DATASET AND BENCHMARK
RobustSpring is large, novel image corruption dataset for optical flow, scene flow, and stereo. Below, we describe how we build on Spring's stereo video dataset and augment its frames with diverse image corruptions integrated in time, stereo, and depth, how we evaluate robustness image corruptions, and use it to benchmark algorithm capabilities.
Spring (Mchl et al. 2023b) is a high-resolution benchmark with rendered stereo sequences and dense ground truth. It the ideal base for an image corruption dataset as its detailed renderings permit image alterations of varying granularity from removing detail by blurring to adding detail via weather. Spring provides a public training and closed test split, where test ground truth for optical flow, disparity, and extrinsic camera parameters is withheld. As RobustSpring designed to complement accuracy analyses, we build on the 2000 Spring test frames (two per stereo camera). To apply corruptions with time, stereo, and depth consistency, we require depth and extrinsics that are not publicly available. We therefore estimate extrinsics using COLMAP 3.8 and depths via Z = f==B'' with focal length fz. baseline B, and disparities d predicted by MS- RAFT+ (Jahedi et al. 2022 2024). This estimation avoids ground-truth leakage while maintaining benchmark integrity. Quantitative results on the accuracy of our depth and extrinsics estimation are given in App. A.5 and detailed discussion of motion ranges in Spring is provided in App. A.7
Spring Data.
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Color
Blur
Noise
Qual
Weather
Defoxue blur
Guussan Mur
Glass blur
Motion blur
Brighmess
Contrast
Suturate
Defreus
Goussian
Motion
Coussian
Impulse
Speckle
Pisellate
Elastic
Spatter
Glasss
Zoom
Shox
JPGG
Frondt
Snow
Roin
Rog
Linghnees
Contitd
Property
Time-cons.
Guassian goise
Impuise nols
Specile Rooe
Shot DoGe
Steren cons.
Depth-cons.
0.70
0200
0.70
0.29
020
020
0.70
0.72
0.73
1L70
0.0
071
Foz
SSIM
(b) Overview corruptions and their consistency in time,
Spatter
Fand
Rain
stereo, or depth, with resulting visual changes w.r.t. the original images as SSIM.
(a) Image corruptions on a single image.
Figure 2: Overview of RobustSpring's image corruptions.
3.1 CORRUPTION DATASET CREATION
RobustSpring corrupts the Spring test frames via 20 diverse image corruptions, summarized in Fig. 2a and Fig. 26 Below, we describe the image corruption types, their new consistencies, their implementation, and their severity levels.
Corruption Types. In RobustSpring, we consider the five image corruption types from Hendrycks & Dietterich (019):: color, blur, noise, quality, and weather. Color simulates different lighting conditions and camera settings, including brightness, contrast, and saturation. Blur acts like focus and motion artifacts, including defocus, Gaussian, glass, motion, and zoom blur. Noise represents sensor errors and ambiance, including Gaussian, impulse, speckle, and shot noise. Quality dis- tortions are lossy compressions and geometric distortions, including pixelation, JPEG, and elastic transformations. Weather enacts outdoor conditions, including spatter, frost, snow, rain, and fog. All corruptions applied to the same frame are shown in Fig. 2a
While these 20 corruptions do not cover the entire corruption space, they are chosen represent the most common perturbations encountered in real imagery and to provide a balanced basis for ro- bustness evaluation. Several implementations were adapted and additionally include practically relevant corruptions such as saturation, Gaussian blur, speckle noise, rain, and spatter. The blur and noise families span the most dominant optical and sensor degradations, and the weather and quality corruptions capture major outdoor and compression-relatt turbations, such as illumination changes requiring re-rendering (e.g. colored or dynamic lighting), lie beyond what can be approximated in post-processing. These, along with further outdoor effects (e.g. bloom, glare, dusty conditions) or extended codec distortions (e.g. JPEG 2000), form natural directions for future extensions.
effects. At the same time, some per-
Corruption Consistencies. To increase the realism of these 20 corruptions for dense matching models, extend their definition to time, stereo, and depth: Time-consistent corruptions evolve smoothly over subsequent frames for a single camera, mirroring persistent lens or sensor effect, e.g. frost follows temporally coherent pattern on one camera but differs between left and right. Stereo-consistent corruptions equally influence both stereo cameras, such as shared brightness or contrast adjustments. Unlike simply using the same hyperparameters, stereo consistency does not imply identical pixel-wise noise realizations, only that both views undergo the same transformation strength. Depth-consistent corruptions are rendered directly in the 3D scene, ensuring that their projection into each stereo view respects geometry. This applies to weather effects, such as snow, rain, and fog, where particles follow 3D trajectories and generate view-dependent projections. Other corruptions, such as blur or noise, do not benefit from 3D rendering; therefore, they use independent realizations per frame, despite sharing global severity parameters. Fig. 2bb summarizes the consis- tencies we added to 16 of our 20 corruptions. Note that motion blur is not sterco-consistent because it depends on the specific camera view.
Corruption Implementation. Though most corruptions are loosely based on Hendrycks & Diet- terich (2019), our corruption consistencies require multiple adaptations. Furthermore, we employ specialized techniques for highly consistent effects, i.e. motion blur, elastic transform, snow, rain and fog. We adapt implementations from Hendrycks & Dietterich (2019). modify glass blur, zoom blur, frost, and pixelation accommodate higher resolutions and non-square images, and adjust
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Brightness
Fog
Motion blur
Pixelate
Rain
Speckle noise
Figure 3: RobustSpring example frames. First row shows clean and corrupted images. Second row
shows the left and right disparity maps predicted with LEA Sterco (Cheng et al.] 2020). Third row
shows the target disparities for forward left, backward left, forward right, and backward right direc- tions from M-FUSE (Mehl et al., 2023a). Fourth row shows optical flow estimates for forward left,
backward left, forward right, and backward right from RAFT (Teed & Deng 2020). All disparities and flows are computed on the corrupted dataset. See Fig. in App.
A.3. for additional frames.
frost, glass blur, and spatter for consistency across video scenes. Motion blur is based Zheng ct al. (2006) and adds camera-induced motion with clean optical flow estimates. Elastic transform uses PyTorch's transforms package create see-through-watee waterr-kke effect, changing object mor- phology with smooth frame transitions. For snow and rain, we expand the two-step 3D particle rendering of [Schmalfuus et al. (2023) multi-step particle trajectories and stereo views, change from additive-blending to order-independent alpha blending (McGuire & Bavoil (201)), and include global illumination (Halder al. 2019). To augment the large-scale Spring data, we improve its performance via more effective particle generation and parallel processing. Fog is based on the Koschmieder model following Wiesemann Jiang (001)). Full details are App.
A.3.3
Corruption Severity. Prior works (Hendrycks & Dietterich, 2019 Müller et al. 2023 Kar et al.,
2022 Michaelis al. 2019:; Kong et al. 2023) defined corruptions with several levels severity. Here, we opt for one severity per corruption, because evaluating one scene flow model on all 20 corruptions already produces 2.1 TB of raw data 1.2 GB after subsampling, cf. Sec. 3.2 More severity levels would overburden the evaluation resources of benchmark users. To balance severity across corruptions, we tune their hyperparameters until the image SSIM reaches a defined threshold. We generally SSIM 2 0.7, and, because the SSIM is less sensitive to blurs than noises (Hore & Ziou 2010), SSIM 2 0.2 for noises for visually similar artifact strengths. We conducted a focused perceptual study validate the selection of corruption strengths and their corresponding SSIM values in RobustSpring. See App. A.6 for more details, Final SSIMs are in Fig. 2b
3.2 ROBUSTNESS EVALUATION METRIC
With various corruption types, we need a metric to quantify model robustness to these variations. In the following, we motivate and derive ground-truth-free free robustness metric for dense matching. introduce subsampling for efficiency, and discuss strategies for joint rankings over corruptions.
Definition of Optical Flow. Throughout this work, and consistent with the Spring benchmark (Mehl et al.] 2023b). we define optical flow as the true 3D motion of visible surfaces projected into the 2D image plane (Horn Schunck 1981 Baker et al., (011). This definition is standard in classical benchmarks and provides the basis for accuracy evaluation. Alternative formulations, such as ap- parent motion, cxist in the literature, but RobustSpring's robustness evaluation is agnostic to this choice, since robustness is measured independently of ground truth. A more detailed discussion on the implications of this definition, including the relation between perfect accuracy and perfect robustness, is provided in App.
Robustness Metric Concepts. For dense matching, robustness to corruptions lacks standard- ized evaluation metric. Metrics exist for adversarial robustness, using the distance between corrupt
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prediction and either (i) ground tround-truth (Ranjan al. 2019 Agnihotri et al. 2024c or (ii) clean prediction (Schmalfuss et al., 2022b; 2023| 2022a). The latter is preferred for two reasons: First, (i)ss ground-truth comparisons mix accuracy and robustness. which ties (Schmalfuss et al. 2022b Tsipras et al., 2019, Taori et al. 2020) that should be separate. This competition intuitive: A model that always outputs the same value is robust inaccurate. Like- wise, an accurate model varies for any input change and thus is not robust. Second, (ii) separates robustness from accuracy and builds on an established mathematical concept for system robust- ness (Hein & Andriushchenko 2017 Pauli et al., 2022): the Lipschitz constant L It defines robust models as those whose prediction f similar on clean and corrupt image I and I relative their difference. For dense matching, it reads
competing model quali-
L -(T))
-1-191
where the term || - Iall refers to the per-pixel intensity difference between the clean and cor- rupted images. This robustness formulation is preferable for real-world applications that demand stable scene estimations despite corruptions like snow, and remains valid independent of whether optical flow defined as true motion or apparent motion. We emphasize that RobustSpring ex- plicitly measures robustness in terms of stability: models are considered robust if their predictions remain consistent under corrupted inputs. Lower robustness scores correspond to higher stability, not improved accuracy. Other definitions of robustness, such ground-truth--bed based robustness, possible and may be more appropriate for different use cases. Our benchmark deliberately focuses on stability- based robustness, providing complementary axis of evaluation alongside accuracy.
Corruption Robustness Metric. Based on Eq. ( we quantify model robustness corruptions. Because RobustSpring's corrupt images I. deviate from their clean counterparts I by similar amount, cf SSIM equalization See. 3.I we omit the denominator in Eq. (T) and define cor- ruplion robusiness It as distance between clean f()) and corrupted f(T") predictions with distance metric M:
M)) f(1)).
For similarity to Spring's 's evaluation, we use corruption robustness with various metrics M. reporting REPEs Ripx and RGn for optical and scene flow, and Rpp RAM, and Ri for stereo. Here, EPE denotes the average end-point error, 1px the percentage of pixels with an error exceeding px, Fl the KITTI optical-flow outlier rate. Abs the mcan absolute disparity error, and DI the KITTI disparity outlier rate, see Mchl et al. (2023b) and Menze & Geiger (2015) for more details. Interestingly, our EPE- based corruption robustness
1 (11(()))((T)) O
RipE EPP()(())
Σ
on image domain 52 is a generalization of optical-flow adversarial robustness (Schmalluss et al. 2022b) dense matching and corruptions.
Metric Subsampling. For a benchmark, users should upload robustness results to a web server. Given the large number of 20 datasets, data reduction is essential to facilitate evaluations and up- loads. To this end, we evaluate on a reduced set of pixels by refining the original subsampling strategy from Spring, which retains about 1 % of the full data. First, we additionally subsample the set of full-resolution Hero-frames because they are only kept in the original benchmark for visual- ization purposes, leaving 0.95%, and then apply 20-fold subsampling, ultimately keeping 0.05% of the full data.
Robustness Ranking. Because we generate 20 different corruption evaluations per dense matching model, we need a summarization strategy to produce one result per model. Per-model results are ranked based on three strategies: Average, Median, and the Schulze voting method (Schulze 2018). In contrast to averaging across all 20 evaluations, the median reduces the impact of extreme outliers. The Schulze method provides holistic, pairwise comparison approach that ranks models based on preference aggregation and was used for prior generalization evaluations the Robust Vision Challenges. We evaluate their differences in Sec. 4.2
3.3 DATASET AND BENCHMARK FUNCTIONALITY
Below, we summarize RobustSpring's 's corruption dataset and describe its benchmark function. Fig shows data samples with stereo, optical flow, and scene flow estimates.

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Published as a conference paper at ICLR 2026
Table 1: Initial RobustSpring results on corruption robustness of optical flow models, using REPE Rip and Rij between clean and corrupted flow predictions. Low values indicate robust models. Eclean compares clean predictions with ground-truth flow, values from Mehl et al. (2033b).
SEGRAFT
GMITTO
MSRAFT+
FlanFornee
SPYNet
RAPY
FloeNeet
PWCN
New
Rig
Fi
Fis
Flaxs
Fure
Pl..
Fire
Flg.
Num
Para
Fig
Brigetou
(03)
144
0.35
131
1:13
0.31
3.84
1.00
0.58
2,57
105
0.35
5.53
100
11.0
8.91
0.50
3.66
1.00
0.15
3.65
1.05
1:01
2.14
0.34
1.30
8.58
0.34
2.81
1.30
0.42
2.33
1.55
01.45
3447
1166
1.56
17:34
0.99
9.33
1.47
0.31
3.40
1.00
1.21
9192
3,66
Osaviaa
1.29
Ed6 1.27
1.88
0.38
4.45
1.3
0.59
4.23
1.37
062
5:85
1.22
8076
15:44
2.13
1:10
5:12
2.36
0.50
4.50
1.20
1.31
1:12
5.9
Cliw
3881
0.33
4.5
1.37
0.54
4.44
3.00
1.91
188
5.33
0.59
3.02
308
247
13.75
1:31
7.51
4.7
2,30
19-56
16
6.75
1.45
Zoee
1.53
04.31
8.41
128
15:00
9.00
21.84
7.33
1.66
22.12
6.77
1.54
2511
1.66
4.00
46.07
28.59
3.14
22.80
7.60
2.56
34.55
9.64
533
50,35
1544
Classioo
1/40 12.30
0.34
476
57.05
33
5700
25.14
23
0.56
27.53
2:01
34,01
12.00
1.22
(3.23
14.0
7:43
23.02
18.00
1.30
11:34
5.0
273
26.0
9:9
Iegudia
19:11
16.14
38.35
7.31
23.44
16-0
18.15
330
6.51
2.7
7.48
3.97
Shet
1.53
10:00
3.41
1.52
96.71
13
4.56
31.67
13.17
5.15 36.0
16/1
515
25.11
11.99
1.66
10.44
11.56
6.74
35.64
17.26
1.36
0.57
342
250
25.75
130
(Qalt
1:22
2.40
2.10
0.53
7.34
insd
6.22
34.96
15.97
1.85 93:4 33.60
2.00
01.85
12.03
2.89
42-02
16:96
190
56.00
11:51
190
18:10
3.14
33-13
13.5
2.56
11:00
11.85
244
48.1 1591
Reg
1
11:20
Dan 11:15 1.15
0.91
6.11
0.66
9.66
5.67
11:1
11:11
6:15
1331 15 19:91
1:91 17
7.13
1:34
7.87
16:11
Itac.
16.23
36-97
33.09
1,00 64.20
13.12
19:00
36.14
31.33
11:13
35.90
34.63
35,00
4308
56.15
13:21
76-36
42.41
15.55
11.35
03.11
45:25
41.35
4111
73.31 5715
Spuaee
53.31
30,32
1.5 5750
23.00
0.63
25:22
33.34
1.41
41.35
1:25
36.11
1.71
48:40 33.02
7.58
50.37
19.37
9.33
45.03
25.93
9.31
65.41
4011
Kul Das
6.54
17:48
15.08
3776
3369 91
1.58
1551
10.53
1.11
11:17
9.81
7.51
1347 11:45
1.7
15:56 17.60
6.10
13:55
9.56
15.91
17.43
15.07
11:81
76/1 36.7
1,6
(ale Sawer)
13:35
3,09
1.35
091
16.66
395
0.6 513
3.18
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RobustSpring Dataset. The final RobustSpring dataset entails 20 corrupted versions of Spring, resulting in 40,000 frames, or 20,000 stereo frame pairs. Each corruption evaluation yields 3960 optical flows (990 per camera & direction), 2000 stereo disparities (1000 per camera), and 3960 additional scene flow disparity maps (990 per camera per direction). To discourage corruption fine- tuning and ensure a fair benchmark, we do not provide corrupted training data, only corrupted test data. We separately provide the raw data and a curated dataset for predicting dense matches.
RobustSpring Benchmark. RobustSpring enables uploading robustness results to benchmark website for display in a public ranking. To emphasize that robustness and accuracy are two axes of model performance with equal importance (Tsipras et al.) 2019)) we couple RobustSpring with Spring's 's established accuracy benchmark. Thus, researchers can report model robustness and accu- racy on the same dataset. We provide mock-ups of this integration in App. A.8
4 RESULTS
We evaluate RobustSpring under two aspects: First, we report initial results for 17 optical flow, scene flow and stereo models. Then, we analyze the benchmark evaluation, particularly subsampling strategy and ranking methods.
4.1 INITIAL ROBUSTSPRING BENCHMARK RESULTS
We provide initial results on RobustSpring for selected models from all three dense matching tasks. For optical flow, include SEA-RAFT (Wang et al. 2024b), GMFlow (Xu et al. 20226
MS-RAFT+ (Jahedi et al.
2024). FlowFormer (Huang et al. 2022). GMA (Jiang et al. 2021
SPyNet (Ranjan & Black
2017). RAFT (Teed & Deng 2020). FlowNet2 (1g al. 2017 17). and For scene flow, we evaluate M-FUSE (Mehl et al. 2023a) and RAFT-
PWCNet (Sun et al. 2018)
3D TTedd & Deng. 2021).
For stereo estimation, we evaluate RAFT-Stereo (Lipson et al. 202T),
ACVNet (Xu et al. 2022a), LEAStereo (Cheng al. 2020), and GANet (Zhang et al. 2019). An overview of all models and used checkpoints is in the Appendix in Tab. 4 Importantly, none of these models are fine-tuned to either Spring or RobustSpring data, to assess the generalization capacity of existing models.
Optical Flow. The evaluation results in Tab. D and Fig, 4a show large robustness variations across corruption types. Weather corruptions, especially rain and snow, degrade performance most, while color corruptions have little effect. Model rankings also vary: FlowNet2 performs poorly overall but is the most resilient to noise (Fig. 4b). SEA-RAFT and GMFlow achieve the lowest average R while GMA yields the lowest median, detailed in Sec. 4.2
To examine accuracy- robusttess relations, we compare both in Fig. 4c Accurate models tend to be more robust, though no method excels in both dimensions, and the trend is weak and nonlinear (see
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(c) Accuracy vs. Robustnes.
Figure 4: Accumulated corruption robustness R for optical flow models over all corruptions [left], only noise corruptions [middle], and accuracy vs. robustness [right] with a dashed line rep- resenting a quadratic polynomial fit. Small values are robust (and accurate) models. All other corruption classes color (purple), blur (blue), noise (cyan), quality (green), and weather (yellow) are in App. A.4 in Fig. and Fig. [ shows accuracy vs. Median HEpE-
also median robustness in Fig. 8 Fitting a quadratic regression to Fig. 4c highlights this nonlin- earity. Accuracy and robustness coincide for some corruption families but diverge substantially for others. This stands in contrast adversarial robustness, where a strong accuracy-robustness trade- off is observed (Schmalfuss et al. 20226). The key to understanding these differences is to consider different corruption types individually. Because accurate models need to process fine details in or- der to achieve highly accurate predictions. this attention to details can be exploited by based adversarial attacks in Schmalluss et al. (2022b). For RobustSpring' 's non-adversarial corruptions, accurate models are also relatively sensitive to noise corruptions (cf. Fig. (4)). However, the average corruption robustness is mostly dominated by weather corruptions (cf. Fig. (4a.. where more accu- rate models achieve better robustness values. We identify the reason for this in Sec. 4.2] accurate models retain corruption-inducen motion errors around the particles rather than spreading them onto the background (cf. Fig. 5a This aligns with the results in Schmalfuss al. (2023), which found accurate models to be more robust towards adversarial weather attacks.
Architectural trends also emerge. Transformer models (GMFlow, FlowFormer) perform best over- all but struggle with noise, likely due to global matching. Hierarchical models (e.g. MS-RAFT+) show balanced robustness and may benefit from multi-scale feature processing to cope with quality degradations. Stacked models (e.g. SEA-RAFT, FlowNet2) uniquely resist noise, possibly due to their progressive refinement across layers. Overall, architecture influences robustness to specific corruptions, but no paradigm is universally superior.
Scene Flow. The results for scene flow are in Tab. 2a and include optical flow and target frame disparity predictions for M-FUSE and RAFT-3D. M-FUSE generally produces more robust optical flow across corruptions with a lower average RiPE than RAFT-3D. But both methods suffer sig-
Table 2: Initial RobustSpring results on corruption robustness of scene flow and stereo disparity models, using corruption robustness Rip Rm and R2 between clean and corrupted predictions. Low values indicate robust models. Corresponding Disparity from scene flow models LEAAStereo (s) for M-FUSE, and GANet (8) for RAFT-3D in Tab. 25 Stereo disparity models use Stereo (8) and KITTI (k) checkpoints.
(a) Initial scene flow evaluation.
(b) Initial stereo disparity evaluation.
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Published as a conference paper at ICLR 2026
nificant performance losses for severe weather like rain and noise-based corruptions, e.g. impulse noise. Interestingly, their robustness does not improve compared to conventional optical flow mod- els. Noise and weather corruptions remain challenge for Disparity 2 predictions. Here, RAFT-3D consistently achieves lower robustness scores compared to M-FUSE, but conditions like impulse noise rain still notably affect disparity predictions. Overall, both models have limited robustness, but temporal consistency may contribute to lower robustness scores under several corruption types.
Stereo. Results of stereo disparity estimations are presented in Tab. 2b The effect of the different corruptions on the performance is significant, with noise and weather-based corruptions leading to the largest errors, especially for GANet and LEAStereo. In particular, Gaussian and impulse noise introduce extremely large errors, highlighting the sensitivity of stereo models to pixel-level noise. Blur distortions, especially zoom blur, also have a severe impact on all models. contrast, color- based distortions generally yield smaller errors. RAFT-Stereo shows stronger resilience across most corruption groups, performing better on color and noise based corruption than other models, but also struggles with noise and severe weather effects such as rain and snow.
Table Evaluations of the metrics used in RobustSpring.
(a) Influence of subsampling. Wc compare robustness evaluations the full test data (Full) evaluations on Spring's 's original subsampling (Spring). original subsam- pling without framme (Spring*), and our refined cor- ruption subsampling (Ours).
(b) Robustness ranking of optical flow models with ranking strategies Average Rip,, Median Rirs, and Schulze to summarize results over corruptions. Please note that Schulze does not produce numeric values.
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4.2 METRICS AND BENCHMARK CAPABILITY
After reporting initial RobustSpring results, we analyze aspects of its benchmark character: The subsampling strategy for data efficiency, and different ranking systems for result comparisons across 20 different prompt variations. We also validate our robustness metric for object corruptions and explore RobustSpring's 's transferability to the real-world.
Subsampling. We evaluate RobustSpring's ' strict data subsampling by comparing to results on the full test set. As shown in Tab. Bal our subsampling strategy produces results that are nearly identical to those that include all pixels in the robustness calculation. We observe the largest discrepancy for Spring's 's original subsampling, because it includes handful of full-resolution Hero-frames. If those frames are also subsampled (Spring*), results align with the full dataset. Overall, our stricter subsampling to 0.05% of all data is not only data efficient but also exact.
Metric Ranking, To explore how ranking strategies influence the optical-flow robustness order, we contrast our three summarization strategies: Average, Median, and Schulze (cf. App. A.4.3) The rankings in Tab. 3b) notably differ across strategies. The Average differs most from the other rankings. For example, it ranks GMFlow 2nd, which is only 5th on Median and Schulze, suggesting good performance across corruptions without excessive outliers but no top performance on most corruptions. Interestingly, Median and Schulze rankings are more aligned. As Schulze's ranking involves complex comparisons of per-corruption rankings and must be globally recomputed for new models, the Median ranking is a cheap approximation to it. The ranking strategy has significant implications for selecting robust models. While SEA- is optimal across rankings, the rankings accentuate different aspects: overall performance, outlier robustness, or balanced performance in pairwise comparisons. Hence, RobustSpring reports them all.
Robustness on Object Corruptions. In RobustSpring, robustness is defined as stability of pre- dictions under corrupted inputs: models robust if their outputs remain consistent for clean and

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Published as a conference paper at ICLR 2026
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ruption. Robustness scores and rankings remain stable
even if no rain pixels are in the figr calculation.
2015) for most optical flow models.
Figure 5: Additional evaluations of RobustSpring's benchmark character.
corrupted scenes (Eq. Dp. A natural question is whether this metric remains valid when corrup- tions introduce moving objects, such as rain or snow. To test this, we separate the contributions of background and corruption pixels by excluding pixels of objects like rain drops from the score cal- culation. Object pixels are detected via the value difference d between clean and corrupted images, and excluded (1 d) exceeds a threshold. Threshold excludes none (the vanilla (PP ) while threshold 100 excludes all. Figure [a shows the robustness score if rain is excluded from the cal- culation, along with bars indicating the amount [%] of excluded pixels. Remarkably, the robustness score changes by at most 5% even when all rain pixels (about 90% of the image) are discarded. High scores for rain or snow (ef. App. AA.444) thus result mainly from mispredictions in the periphery of altered pixels, not from motion predictions on altered pixels. As scene-wide effects dominate, our stability-based robustness yields consistent rankings suited for broad robustness evaluations.
Robustness in the Real World. Finally, investigate if RobustSpring's corruption robustness transfers to the real world. To this end, we select the noisiest 10% KITTI data, estimating noise as in Immerkacr (1996). These noisy KITTI frames have clean counterparts calculate corruption robustness H Thus, we approximate H2pe via the accuracy difference on noisy and non-noisy KITTI frames. To account for model-specific performance differences on Spring and KITTI, we
normalize with the clean dataset performance and show the resulting relative robustness Ri in Fig. (b] Relatively robust models with low scores on RobustSpring are also robust on KITTI and vice versa. The only outlier, FlowFormer, overperforms KITTI, potentially due to outstanding memorization capacity and exposure to KITTI during training. Because overall noise resilience on RobustSpring qualitatively transfers KITTI, RobustSpring supports model selection for real-world settings where corruption robustness cannot be measured.
CONCLUSION
With RobustSpring we introduce an image corruption dataset and benchmark that evaluates the ro- bustness of optical flow, scene flow, and stereo models. We carefully design 20 different image corruptions and integrate them in time, stereo, and depth for a holistic evaluation of dense matching tasks. Furthermore, we establish a corruption robustness metric using clean and corrupted predic- tions, and compare ranking strategies to unify model results across all 20 corruptions. Robust- Spring's 's benchmark further supports data-efficient result uploads to an evaluation server. Our initial evaluation of 17 optical flow, scene flow, and stereo models reveals an overall high sensitivity to cor- rupted images. As our robustness results translate to real-world performance, systematic corruption benchmarks like RobustSpring crucial uncover potential model performance improvements.
Limitations. Due to its benchmark character, we have limited the image corruptions on Robust- Spring selection of 20. While this does not cover the full space of potential corruptions, this dudget limitation is necessary to make the RobustSpring dataset applicable and not overburden the computational resources of researchers during evaluation.
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ACKNOWLEDGEMENTS
Lukas Mehl, Jenny Schmalfuss, and Victor Oei acknowledge funding by the Deutsche Forschungs- gemeinschaft (DFG, German Research Foundation) Project-ID 25 251654672 TRR 161: Quantita- tive Methods for Visual Computing (B04, A07). Jenny Schmalfuss and Andres Bruhn acknowledge support by the Deutsche Forschungsgemeinschaff (DFG, German Research Foundation) Project- ID 533085500- Robust Optical Flow. Shashank Agnihotri and Margret Keuper acknowledge sup- port by the DFG Research Unit 5336 - Learning Sense (I.2S). Jenny Schmalfuss and Victor Oei acknowledge support from the International Max Planck Research School for Intelligent Systems (IMPRS-IS).
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A APPENDIX
A.1 DISCUSSION OF ROBUSTNESS DEFINITION
Scope of RobustSpring's robustness definition. Robustness is multifaceted concept. In Robust- Spring we focus on one specific and mathematically established perspective: robustness as predic- tion stability under input corruptions. This choice is motivated by its independence from ground- truth definitions of optical flow, and by its practical utility in assessing whether models maintain consistent scene estimates in the presence visual disturbances. Other notions of robustness, such as those directly tied to ground truth, are equally valid but outside the scope of this benchmark.
Accuracy vs. Robustness. RobustSpring explicitly disentangles accuracy and robustness. Accuracy measures the deviation between model predictions and ground truth, which depends on the adopted definition of optical flow flow-hert here, the true 3D motion of visible surfaces projected into the 2D image plane (Hom & Schunck 1981 Baker al. 201T). Robustness, by contrast, is defined via Lipschitz continuity (Eq. [ as the stability of predictions under corrupted inputs, independent of ground truth.
Why Ground-truth--fre Robustness. Some works define robustness as the difference between predictions on corrupted images and the corresponding ground truth (Ranjan et al. [019 Agnihotri jet al., 2024c). This approach entangles robustness with accuracy and requires ambiguous judgments about how each corruption changes the motion field (e.g. elastic transforms, rain, snow). By adopt- ing a ground-truth- robustness literature (Schmalfuss al.) 2022b: Tsipras et al. 2019: Taori et al. 2020 Hein & An- driushchenko 2017: Pauli et al. (022).
definition, RobustSpring avoids this ambiguity and aligns with established
Perfect Accuracy Perfect Robustness. Under our definitions, perfectly accurate method may achieve poor robustness scores for corruptions that genuinely alter the motion field (e.g. snow, elas- tic transform), because its predictions change in accordance with the altered scene. Conversely, perfectly robust method (constant predictions) is maximally stable but inaccurate. This disentan- glement is intentional: it reveals cases where accuracy and robustness align versus diverge. Such divergence highlights model behavior that plain accuracy metrics cannot capture.
Practical Relevance, As demonstrated in Fig. 5a current models tend to propagate spurious fore- ground motions introduced by rain into the background, leading to degraded scene estimates. Ro- bustSpring's 's robustness metric captures this instability and thus provides valuable insight into real- world performance. The combination of accuracy and robustness offers practitioners two-axis evaluation, allowing trade-offs between precision and stability depending on the target application.
A.2 LINKS AND CHECKPOINTS FOR EVALUATED MODELS
We evaluated the original, author provided optical flow, scene flow and stereo methods on the Ro- bustSpring dataset. Tab. reports the repositories and checkpoints for the optical flow, scene flow
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Table 4: Repositories and checkpoints used for evaluating methods in RobustSpring. *The Mixed checkpoint MS-RAFT+ is pretrained on Chairs and Things and then fine-tuned on a mix of Sintel, Viper, KITTI 2015, Things, and HDIk.
Method
Repository
Checkpoint
Optical Flow
RAFT
Ettps:/ /ttp:://///tbb..tt httmm//rnnstttor//// /RA/TT
Sintel
PWCNet
/ PPWC-Net
Sintel
GMFlow
REtp5:/ /ggatb.......................... com/ m3ore1 xu, qmr L69
Sintel
GMA
Pttpst/
Sintel
FlowNer2
A httpt///gthtb..oom com/ /////WDDDD WWTTDA/ Townet2 pycored
Sintel
FlowFormer
ffficial
Sintel
MS-RAFT+
A LLLpt77791 wttp://ggnbb...... con/cy scut Lastt78 RAPT pi0s
Mixed
SEA-RAFT
BEtps://
wwtp/etrnnneeeeoeeo VI7SEA
Spring (M)
SPyNel
Estp5:/ htppss//qstnube com/ aniraggaa 1/1otttt PPTTcch implementation)
Sintel
Sttps://gatnnn www.a///a/naaaaa.............a.a apyynet [onginal implementation)
Scene Flow M-FUSE
https://githu..coo/// stuttggrrttttttttttat FUSE
KITTI 2015
Stereo RAFT-Sterco
htts:///github.comm//e v1 /RAPT Stereo/
Scene Flow (S)
ACVNet LEAStereo GANet
Scene Flow (S)
(Lps://91ttnie com/ Xue wew.eeascceee.. sem LeASterec
Scene Flow (S), KITTI (k)
Sctps://gstniie Feihuzaang
Scene Flow (8), KITTI (k)
and stereo models, which were benchmarked on RobustSpring in Tab. Tab. 2a and Tab. 2b Fur- ther details on training and checkpoints for these models can be found in their original publications.
Ressources and Run Times. We conducted all evaluations on a single NVIDIA RTX A6000 (48 GB) GPU. The evaluation time of models on the RobustSpring data strongly depends the compu- tational efficiency and requirements of the original models. As a representative example, the optical flow evaluation with RAFT (Teed & Deng 2020) took 20 hours on the full RobustSpring data.
Once the methods are evaluated and the results uploaded to our benchmark server prototype, the ro- bustness evaluation for optical flow method takes 13 minutes Xeon(R) Gold 6130 CPU @ 2.10GHz, 8 vCPUs). The robustness evaluation for scene flow takes about 26 minutes, and minutes for stereo.
evaluation machine (Intel(R)
A.3 ROBUSTSPRING IMAGE CORRUPTIONS
Below, we provide supplementary information on the image corruptions for the RobustSpring dataset. Besides visualizing further benchmark samples and supplying video that showcases the space- and time-integration of our corruptions, we also give details on their implementation with focus RobustSpring-specifn consistencies.
A.3.1
ADDITIONAL CORRUPTION BENCHMARK SAMPLES
To complement the benchmark samples in Fig. we show benchmark results on additional corrup- tions in Fig.
A.3.2
VIDEO OF CORRUPTIONS
With the supplementary material, we include a video that visualizes all corruptions applied to the Spring test dataset.
A.3.3 CORRUPTION IMPLEMENTATION
Below we provide the implementation details and parameters for all corruptions in the RobustSpring dataset. We cluster the corruptions by their main classes. The original (uncorrupted) image is denoted as I, while the corrupted version is f. The pixel-aligned depth values are D. In the stereo video setting, the image subscripts and + 1 denote frames over time, while I and denote left and right frame, where necessary. All of RobustSpring's noises are independent of time, stereo and depth, which means they are sampled independently for every single image of the dataset.
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Contrott
Sanuate
IPEG Compecssion
Elastic Transfon
Spatter
Frost
Saow
Defscus Blur
Guurecann Blur
Glaoes Blur
Zoom Ble
Guussiu Noise
Ingulee Noise
Slad Noise
Figure RobustSpring example frames, complementing Fig. 3 The first row shows clean and corrupted images. The second row shows the left and right disparity maps predicted with LEA Stereo (Cheng et al., 2020). The third row shows the target disparities for forward left, backward left, forward right, and backward right directions from M-FUSE (Mehl et al. 2023a). The fourth row shows optical flow estimates for forward left, backward left, forward right, and backward right from RAFT TTed & Deng 2020). All disparities and flows are computed the corrupted dataset.
Brightness. The brightness is adapted via
i=I+c.
(4)
and for time- and stereo-consistent brightness changes in RobustSpring we choose the parameter c of = cf 4+1 c++1 0.39.
Contrast. The equation to adapt contrast is
= (T mean ()) - c++ mean(T),
(5)
where we selected c c = c = ct+1 = -c+1 = 0.16 for time- and stereo-consistent contrast adaptations.
Saturation. For those adaptations the RGB image is transformed to HSV, and the saturation com- ponent S is adapted via
S-
with af of a/41 at+1 stereo-consistent saturation changes.
2.3 and 8 B5 Bl41
B441 0.01 for time- and
Defocus Blur. The defocus blur convolves the image with circular mean filter mmean
i= 1 Cimean
where we choose the radius = =] = rf r/41 rf+1 6 for time- and stereo-consistent
blurring- Gaussian Blur. Gaussian blur convolves the image with Gaussian Coauss
I Gauss
(8)
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where choose the standard deviation oj of =0411 0441 4 for time- and stereo- consistent blurring.
Glass Blur. This is Gauss-blurred image, whose pixels afterwards shuffled via the shuffling r)) over several iterations within a neighhorhood of radius r
= 551 C ,2,7),
where Iwo sets of time-consistent parameters are picked for the different stereo cameras: of d'1 = 1.2, of = o++ = 1.2, & 1 = 1, x = 1441 = 1, 1, = r/+1 = = 3 and r = rh+1 3.
Motion Blur. Motion blur is implemented by averaging the intensities of pixels along the motion trajectory determined by the optical flow. Let v(æ, y) (v, v), (z, y)) be the optical flow vector pixel (z, y). and let
N max (1. (1.[10-
max [[ will w (n.y)
(10)
be the number of samples along the motion path. Then, the blurred pixel is computed as
N
f(z,8) v) = N+
Σ km0
I
x (z, v + N (2(22,))
(11)
Here, the scaling factor 10 controls the extent of the blur relative to the magnitude of the motion.
Zoom Blur. Zoom blur created by averaging the original image with a series of zoomed-in versions of itself. Specifically, let 2(1, z) denote the image I zoomed by a factor z, and let {=;} be a set of zoom factors ranging from to approximately 1.24 (in increments of 0.02). Then the final image is computed as
T
Z(,
Σ
(12)
N
where N is the number of zoom factors. This formulation averages the original image with its progressively zoomed versions, resulting in a smooth zoom blur effect.
Gaussian Noise. This noise adds a random value from a Normal distribution every pixel in the original image, where (H, o²) is T-shaped array of random numbers that are drawn from the Normal distribution with mean j and variance o²:
a N7(0,1).
(13)
The scaling a = 0.115 is selected for all images in RobustSpring, but .N 1) is sampled anew for every image.
Impulse Noise. Here, for a fixed fraction of pixels their values are replaced by the values 0 or 255. For RobustSpring, p = 0.075.
Speckle Noise. Like Gaussian noise, Speckle noise also builds on random values from Normal distribution, but adds these values after additionally scaling with
- I-II-- (7 1).
(14)
For RobustSpring, the parameter is 0.45.
Shot Noise. Shot noise uses values drawn from a Poisson distribution P per pixel
i= P(T.c)
(15)
c
where c = 23 for RobustSpring.
Pixelation. This is achieved by downsampling the image to size s, fraction of its original size, with a box filter boz (I, s), followed by upsampling up s) to the size s, which is the original size:
uporor (bborbr, fc),, T)-
(16)
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15
Sererolie
8-
4-
SEA-RAFT
GMIFtw
AIS-RAFT
Farmer
GM4
APydle
RAFT
Fleeffrdd
PWERerr
SEA-RAFT
GMFFle
MS JSSRA
Flowfarmer
GMM
SPS
BAPT Plavsies PWCkat
(a) Color corruptions.
(b) Blur corruttions.
100
25
SEA-BFF
GIMFow
M8-R4FT+
FloeFrmer
Gidd
SPy
RAPT
SEA SEA-RAFT
GMPlow
MR-EAPT+
FlomFormer
Gid
sPyNi
RAPT
(c) Quality corruptions.
(d) Weather corruptions.
Figure 7: Additional results on accumulated corruption robustness REPE for optical flow models over corruption classes color, blur, quality, and weather. More results are in Fig. 4
For RobustSpring, stereo-consistent pixelation.
the size fraction c ce ci -++ = c++ 0.16 for time- and
JPEG Compression. For JPEG compression, the quality is the only variable parameter
i i= JPEG(L,g))
(17)
which selected as q = of = di = d.t = Et = for time- and stereo-consistent JPEG compression.
Elastic Transformation. The elastic transformation applies an elastic deformation using torchvis i on trans forms. v2 with parameters = 110.0 and a = 5.0 to control the de- formation magnitude and smoothness, while preserving the original frame dimensions.
The spatter corruption simulates liquid droplets by generating a liquid layer from Gaussian noise, applying blur and thresholding, and blending with the original image using a predefined color.
Spatter.
The frost corruption overlays frost texture onto the image by randomly selecting and resiz- ing a pre-stored frost image and blending it with the input to create an icy appearance.
Frost.
Snow and Rain. The implementation for snow and rain is based on Schmalfuss et al. (023). with methodological and performance improvements. On the methodological side, we replaced additive blending with prder-independent alpha blending (Meshkin's 's Method, McGuire & Bavoil (201)) and included global illumination (Halder et al. 2019) in the color rendering. Also, we expanded the monocular two-step motion simulation to multi- stereo images. On the performance side, we introduce an efficient parallel particle initialization and improve the parallel processing performance.
Fog. Fog is modelled using the Koschmieder model from Wiesemann & Jiang (0066 as
i=1-e"
(-10(20)
+1. e
D-In(30)
(18)
where dm is the visibility range and I the luminance of the sky. For RobustSpring, we use dn 45 and = 0.8. As it directly depends on the depth D, this is depth-consistent and due to its integration into the 3D scene it is also stereo- and time-consistent.
A.4 ADDITIONAL EXPERIMENTAL RESULTS
Below, we expand on the experiments in the main paper and provide supplementary results for our major experiments.
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Robustness RÉpe [Med]
3.0
SEA-RAFT
GMFlow
2.5
MS-RAPT+
FlowPormer
2.0
GMA
SPyNet
RAFT
1.5
FlowNet2
PWCNa
1.0
1
3
5
6
Accuracy EPE
Figure 8: Accuracy vs. robustness of optical flow methods, measured as EPE and median Repe- Small values indicate accurate and robust methods. The dashed line represents quadratic polyno- mial fit. Fig. shows the average REPE
Table 5: Pairwise comparison matrix for the Schulze method.
SEA-RAFT
MS-RAFT+
FlowFormer
GMFlow
PWCNet
FlowNer2
SPyNet
GMA
RAFT
SEA-RAFT
0
14
14
14
14
17
17
15
19
GMFlow
6
9
14
9
10
16
14
MS-RAFT+
6
9
0
15
11
11
19
12
15
FlowFormer
6
5
0
3
12
16
13
GMA
6
11
9
17
0
12
20
10
15
SPyNet
3
8
8
RAFT
3
4
1
4
0
7
0
4
9
FlowNet2
5
12
10
16
16
18
PWCNet
1
5
7
5
7
11
2
0
A.4.1 CORRUPTION ROBUSTNESS BY CORRUPTION GROUP
Figure shows the corruption robustness REpE for each optical flow method across the remaining four corruption groups in addition to Fig. + in the main paper. It underlines the varying degrees of robustness of the evaluated methods against specific types of corruption.
A.4.2 ACCURACY MEDIAN CORRUPTION ROBUSTNESS
In Fig. g we show the ceuuracy-robusteess evaluation with the Median corruption robustness, to complement Fig. wchicc uses the average corruption robustness. Even though the robustness rank- ing of methods varies between average and media corruption robustness, cf. Sec. 4.2 for discussion on ranking differences, the general trend that corruption robustness and accuracy are weakly corre- lated remains. However, there is still no clear winner, and robuuteyss tradeoff persists among particularly accurate or robust methods.
A.4.3 SCHULZE PAIRWISE COMPARISON MATRIX
The Schulze method is ranking algorithm used determine the most preferred candidate based on pairwise comparisons. We include a pairwise comparison matrix in Table 5 for our ranking. The table shows how often the method in row is better than the method in column j. based on the number of corruptions where method achieves lower error than method j. The ranking process consists of the following steps:
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40
PWCNet
FlowNet2
Robnstness RiPE
SPyNet
Excluded Pixels [96]
30
FlowFormer
RAFT
GMA
20
MS-RAFT+
GMFlow
10
20
20
60
100
Snow Detection Threshold [%
Figure 9: Stability of corruption robustness Hir for snow corruption. Fig.
Analogous rain results
5a
1) Constructing the Pairwise Comparison Matrix. For each pair of methods, we count how many times one method achieves lower EPE than the other across different corruptions. If method A has lower EPE than method B in given corruption, the corresponding entry in the matrix is incremented.
2) Computing the Strongest Paths. We define the strength of a path from method A to method B as the number of cases where A outperforms B. The strongest paths between methods are determined by considering indirect paths: if method A is better than method B, and method B is better than method C, then the strength of the indirect path from A to considered.
3) Determining the Final Ranking. Method A is ranked higher than method B if the strongest path from A to B stronger than the strongest path from to A. This ensures that even if a method loses to another in some comparisons, it can still be ranked higher if it consistently performs well against other methods.
A.4.4 CORRUPTION ROBUSTNESS ON SNOW
Finally, we complement the evaluation of our corruption robustness metric in the presence of rain in Fig. 5a with the corresponding evaluation in the presence of snow in Fig. 9 The results with rain translate to snow with the following minor differences: Because snow has less motion blur than rain, it covers fewer pixels (60% of all pixels vs. 90% for rain). For snow, the score drops bit more than for rain when object pixels are excluded (525% drop vs. <5% for rain), potentially consequence of the increased object opacity for snow particles. Still, the background error (2 75% contribution to corruption robustness) dominates the score, and the robustness ranking for optical flow methods remains stable, whether snow pixels are included in the score calculation or not. Hence, the additional evaluation on snow further substantiates the stability and expressiveness of corruption robustness evaluation metric.
A.4.5 REPRODUCIIBILITY
We compute the noise in the KITTI (Geiger et al. (2012) frames according to Immerkaer (1996), and select the top 10% noisiest frames. The final frames and their noise levels are listed Tab. 6
A.5 DEPTH AND EXTRINSICS ESTIMATION FOR CORRUPTIONS
Disparity and Depth. For depth-dependent corruptions, we estimate disparity maps with MS- RAFT+ (Jahedi et al. 2022 2024). On the Spring validation set, we find mean EPE of 1.09 px and D1-all of 5.60%. Mean EPE measures the average Euclidean distance between predicted and ground -oound-truth disparities, while DI-all reports the percentage of pixels with disparity error larger than px 5% of the ground-truth disparity. These emror sufficiently small to support realistic
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Table 6: Top 10% noisiest KITTI frames with noise estimation from Immerkaer (1996). Frames
from kitti ///aanse//raanin/iagge
000061 10pmg
000060 10.png
10.ppg
000068 10.png
000143 100.pm
O000 10..pgg
000124 10.png
10.png
000101 10.png
000175 10.pme
0000644 10.pmg
00006s 10.pmg
0000600 10.png
000000 10:png
10000 10ppg
Comporo ss0000
Budror PST000
Budry 891000
sudro T
Rud011 150000
000065 000065_10gpp
000142.11000
000003
000142
L$1000
0000
Frame
Noïse 3.54 3.22 3.07 2.92 2.80 2.79 2.77 2.74 2.69 2.66 2.62 2.61 2.61 2.60 2.48 2.47 2.46 2.46 2.43 2.41
augmentations, Note that depth estimates are used by only 3 of the 20 corruption types (fog, snow, and rain; motion blur uses depth only implicitly).
Extrinsics. We estimate camera extrinsics using COLMAP 3.8. As trajectories are not expressed common reference frame, quantitative pose metrics such as absolute trajectory error (ATE) or relative pose error (RPE) cannot be meaningfully computed without a rigid alignment step, which itself introduces error. Extrinsics are required for only 2 corruptions (snow, rain). We therefore evaluate them qualitatively: the resulting augmentations exhibit realistic motion behavior for both effects.
A.6 USER STUDY ON SSIM THRESHOLDD
The SSIM thresholds of 0.7 for color, blur, quality, and weather corruptions and 0.2 for noise were chosen empirically to yield comparable perceptual corruption strength across categories.
We conducted a focused perceptual study validate the choice of corruption strengths and their as- sociated SSIM values in RobustSpring. For five representative corruptions (one per main corruption family: contrast, fog, Gaussian blur; Gaussian noise, and pixelation), participants repeatedly viewed clean Spring frame alongside three corrupted versions. See Figs. I0 and [I for examples. They then selected the corrupted image that looked most like a "reasonably" corrupted version of the clean frame. This means the image is clearly degraded, inducing distribution shift, yet still recognizably close to the original. Across 14 user studies, with 10 samples each, for a total 140 comparisons, observed agreement rates ranging from 70% to 100% from each user for the SSIM values used in our study. The average agreement was 87.14%, which suggests that the SSIM ranges used for these representative corruptions align with human perception of realistic, moderate corruption strengths.
In the user study, the web interface displayed one clean reference image from Spring and three corresponding corrupted versions of the same image on each page. These images were obtained by applying single corruption type at three different intensities. We applied thresholds for three levels of severity: low, medium, and high, with corresponding SSIM values of 0.95, 0.7, and 0.2, respectively. The three corrupted images were randomly ordered, and participants were asked to select one image per page that they perceived as the most reasonably corrupted version of the clean image. The corruption should be clearly visible and sufficient cause distribution shift but not so strong that the scene appears implausible or too different from the original image. This setup was repeated for the five corruption types listed above, and for each page and participant, the selected intensity was recorded. This allowed us to compute per-participant agreement with the nominal level and the aggregate agreement statistics reported above. In each scenario, the nominal value corresponds to the SSIM value chosen for the respective corruption in our work.
A.7 MOTION RANGE OF SPRING
As discussed the Spring paper (Mehl et al. 2023b), the dataset covers wider range of motion than standard benchmarks. While Sintel (Butler et al. 2012) and KITTI (Menze & Geiger 2015) align with Spring for smaller motions (u: 500 to 500, v: 250 to 250), Spring extends the range up to 1700 (u) and -50 (v). This corresponds to motion magnitudes about 1.5-3 times larger than those in Sintel and KITTL We highlight this here as one reason for selecting Spring as the foundation of RobustSpring.
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(a) Clean image.
(b) Low severity.
(c) Medium severity (best).
(d) High severity.
Figure 10: Clean image and three corrupted versions with different severity levels for fog used in the user study on SSIM thresholds. "Best" is the one we choose in RobustSpring.
(a) Clean image.
(b) Low severity.
(c) Medium severity.
(d) High severity (best).
Figure 11: Clean image and three corrupted versions with different severity levels for Gaussian noise used in the user study SSIM thresholds. "Best" is the we choose in RobustSpring.
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A.8 INTEGRATION INTO THE SPRING BENCHMARK WEBSITE
To illustrate how RobustSpring extends the existing Spring benchmark infrastructure, we provide mock-ups of the website with our additional robustness results. These examples demonstrate how robustness values are integrated alongside existing accuracy metrics.
Overview Page Fig. 12 shows the modified overview page for optical flow methods. Three new columns report the average robustness scores. This extension allows users to compare methods across both accuracy and robustness at glance.
displays the detail page for a single optical flow method. In addition the accuracy results already present in Spring, robustness values are provided per corruption type, as well as aggregated average and median robustness values. This detailed view enables users to assess strengths and weaknesses of individual methods with respect specific corruptions.
Method Detail Page. Fig. 13
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Figure 12: Modified Spring benchmark overview page for optical flow. Newly added robustness columns are highlighted red.
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Figure 13: Modified Spring benchmark method detail page for optical flow. Robustness values are reported for each corruption, well as aggregated average and median scores. Newly added sections are highlighted in red.
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Reviews:
[{"review_id": "QiVyGZgkF0", "reviewer": "Reviewer_gjCL", "summary": "This paper introduces RobustSpring, a large-scale benchmark designed to systematically evaluate the robustness of optical flow, scene flow, and stereo models against 20 types of image corruptions, such as blur, noise, compression artifacts, and weather effects. The benchmark extends the Spring dataset by applying corruptions that are consistent in time, stereo, and depth. A new corruption robustness metric based on Lipschitz continuity is proposed to quantify model stability without relying on ground truth. Experiments across 16 models demonstrate large robustness variations across corruption types, and analyses show that robustness partially correlates with accuracy and transfers to real-world data.", "questions": "See the weakness.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "(1) Comprehensive Benchmark Design. The paper introduces the first unified benchmark for evaluating robustness across optical flow, scene flow, and stereo tasks, with a carefully designed corruption taxonomy and consistency mechanisms.\n\n(2) Principled Metric and Evaluation Protocol. The use of a ground-truth-free Lipschitz-based robustness metric provides a theoretically sound and practical approach to isolate robustness from accuracy, avoiding common confounds in prior evaluations.\n\n(3) Extensive and Systematic Experimental Validation. The authors benchmark a wide range of models, analyze architecture-specific trends, and validate the transferability of robustness metrics to real-world datasets (e.g., KITTI), reinforcing the benchmark’s practical relevance.", "weaknesses": "(1) Limited Analysis Beyond Benchmarking. While the benchmark is well-motivated, the paper mainly presents benchmark results without deeper analysis of why certain architectures or design choices improve robustness (e.g., effect of global vs. hierarchical feature aggregation).\n\n(2) Single Severity Level per Corruption. Only one corruption severity level is used to reduce computational cost, which limits fine-grained analysis of model sensitivity. A scalability study across multiple severities could provide more insight into robustness trends.\n\n(3) Lack of Ablation or Analysis Experiments. The work could benefit from additional analysis experiments, such as varying the SSIM threshold used for corruption tuning, testing the effect of different subsampling ratios, or studying the sensitivity of the Lipschitz-based robustness metric’s formulation.\n\n(4) Limited Discussion of Generalization and Scope. The paper acknowledges only briefly that the benchmark does not cover the full corruption space. A clearer discussion of future extensions (e.g., dynamic lighting, motion artifacts beyond the current set) or domain-specific customization would strengthen its impact.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "xOiwu2WCau", "reviewer": "Reviewer_6GQ8", "summary": "The paper updates a recently published benchmark for optical flow and scene flow (SPRING , Mehl et al 2023) so that it also includes image corruptions (e,g. blurring the image, simulating bad weather etc.). It also provides a metric for evaluating algorithms and provides an initial benchmark of existing algorithms.", "questions": "Can you formulate any insights you have learned from the current benchmarking?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 1, "strengths": "Datasets help advance the field of computer vision and it is important to evaluate algorithms in more challenging settings (especially since the Spring dataset is based on computer graphics and there is always the danger that algorithms will overfit to the particulars of the renderer).", "weaknesses": "I am afraid that the contribution is too limited in my mind for a conference publication. The results beyond the introduction of the dataset are very limited and I did not see any insights that have been obtained so far from using this dataset. The contribution would be better appreciated in a \"datasets and benchmarks\" track in a major conference but as far as I know, ICLR does not have such a track.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zwMLOtP0P5", "reviewer": "Reviewer_qH6X", "summary": "This paper proposes RobustSpring, a new benchmark that extends Spring with various augmentations. The authors also propose novel corruption robustness metrics along with efficient subsampling methods for evaluation. Detailed experiments are provided.", "questions": "I have several questions about the metrics and list them below.\n\n- [Object Corruptions] What does \"value difference\" mean (Line 453)? Is that something similar to the intensity difference? Since the rain/snow effects are rendered in blender, why does the paper uses intensity difference instead of directly obtaining rain/snow pixels from rendered results?\n- [Relative Robustness] What does this metric mean (Figure 5b)?\n- How do corruptions affect the model performance? From Table 1 I can only tell whether a model is robust to different corruptions, but do these corruptions always make the predictions harder? If a corruption actually makes the prediction easier (e.g. better EPE), should we include this data point into proposed robustness metrics? \n\nI'm happy to adjust my score if my concerns are addressed.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "RobustSpring introduces a large, diverse corruption benchmark for optical flow, scene flow, and stereo, enabling consistent robustness evaluation across real-world perturbations. It provides new metrics along with efficient subsampling evaluation, promoting robustness as a core research goal.", "weaknesses": "I describe my concerns in \"Questions\".", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "IVhCYEdQ9U", "reviewer": "Reviewer_oJUR", "summary": "The paper proposes a dataset and benchmarking methodology to assess the influence of image corruptions on optical flow estimation, disparity estimation, and scene flow estimation. Following upon similar assessments in the area of image classification, this type of analysis is extended here not only to dense prediction problems but also to correspondence estimation, which necessitates that (some types of) image corruptions are applied in a consistent manner across views. The analysis builds upon the recent Spring benchmark for optical flow estimation, and complements the accuracy evaluation of Spring with a robustness evaluation, which relies on estimating the stability of the prediction w.r.t. the perturbed image and does not rely on any ground truth. An evaluation of several standard models from the literature complements the analysis.", "questions": "* It would be good if the authors could discuss why they chose these particular robustness directions. Yes, they have mostly been considered for image classification, but is it obvious that these are the most important corruption dimensions to consider?\n* Is the snow and fog synthesis really the only place where the consistency goes beyond using the same parameters for all views?\n* Can the authors discuss the finding that accurate models are also robust in light of the desire to decouple the two? Would this analysis perhaps change if we not only normalized the difference in prediction w.r.t. the difference between the perturbed inputs but also w.r.t. the average displacement/disparity? \n* I do not understand completely why the authors used estimated depth etc. to render 3D-consistent artifacts like rain. Put differently, why is leakage of ground truth a worry? In a real scene, rain would be consistent with the true 3D geometry of the scene, so why shouldn’t we use the ground truth depth for synthesizing rain? Sure, this would give some information about the true depth, but so would a capture of a scene with real rain.\n* How are the target SSIM values of 0.7, respectively 0.2 chosen?\n* What are “unoptimized data shifts”? (l. 113)\n* What does the paper mean by “robustness to corruptions is undefined”? (l. 253) If this was really the case, how could this paper assess robustness to corruptions?\n* Which norm is assumed in Eq. 1?\n* Why does it make sense to omit the denominator from Eq. 2? The denominator in Eq. 1 is not based on the SSIM but rather on the norm of the image difference, and fixed a SSIM does not guarantee a certain norm on the image difference.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* A benchmark that assesses the robustness of motion and depth estimation methods w.r.t. image corruptions does not exist so far. This paper extends similar efforts in image classification to correspondence estimation methods and provides a novel and very useful framework to analyze the robustness of motion and depth estimation methods in detail.\n* The benchmark is competently designed and well executed on top of one of the most prominent recent benchmark datasets for motion and depth estimation (Spring). I find it very sensible that robustness is decoupled from accuracy in the evaluation.\n* The proposed benchmark has the potential to become a standard tool for analyzing the robustness of flow and depth estimation methods.\n* Some useful insights emerge from the analysis, such as which family of methods perform well under which conditions. This can allow to design new architectures in the future.\n* The paper is written quite well overall and is easy to follow. Occasionally, there are some odd phrases (see below), but these do not distract majorly from understanding the paper.", "weaknesses": "Major points:\n* The paper does not argue very clearly why the specific set of image corruptions are particularly relevant in the context of motion and depth estimation. A clear justification why these are the prominent corruptions in real-world imagery would strengthen the paper.\n* The notion of stereo, depth and time-consistent corruptions is not clearly defined in the main paper and only becomes more apparent from the supplemental material.\n* I find the notion of stereo, depth, and time-consistent corruptions to be suggesting more than there actually is to it, and also possibly confusing. What the paper seems to want to say with this is that the same noise strength is applied to all views, but that’s not particularly surprising. What’s more: it is also possibly confusing because the reader may, for example, wonder what a stereo-consistent JPEG compression would be. Is it somehow a compression algorithm that considers two frames at once to adapt the compression? Yet, this is not actually the case: all this means is that the compression parameter is the same for both views, which again is sort of a given. The only place where the consistency is non-trivial seems to be the consistent synthesis of rain and snow, which is done in 3D. Details are largely absent though, also from the supplement.\n* I find it surprising that SEA-RAFT (ECCV 2024) has not been included in the analysis. AFAIK, SEA-RAFT is the most competitive member of the RAFT family to date.\n* I am somewhat surprised about the finding that the most accurate models are also the most robust (l. 370f). The paper argues in many places that accuracy and robustness do not necessarily go hand in hand, which I find plausible. Given this, I would have expected this surprising finding to be discussed more deeply.\n* [This might be a contentious point, but one may wonder whether this paper is a good fit for ICLR. This is a pure computer vision paper without any learning component to it and thus would be a better fit for CVPR/ICCV/ECCV. That said, ICLR does include benchmarks in its call for papers and indeed the proposed benchmark allows to assess the robustness of learned motion and depth estimators. I am not taking this point into account in my rating and will leave the assessment of topical fit to the (S)AC.]\n\nMinor points:\n* The metrics in l. 300 should be explained, at least briefly.\n* It does not become clear from the main paper why some frames (“Hero” frames) are being excluded. This becomes apparent only from an experiment in the supplement.\n* Minor language oddities, e.g. l. 115 (models do not report, but papers do), l. 139 (evidence is not a verb), or l. 188 (what does it mean for all corruptions to be on a single frame?).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)