A NeurIPS 2026 challenge on robust dense correspondence under realistic distribution shifts, covering Optical Flow, Stereo Matching, and Scene Flow.
Dense correspondence has made remarkable progress on standard benchmarks. Although modern Optical Flow, Stereo Matching, and Scene Flow methods achieve strong performance under ideal conditions, their accuracy can degrade substantially under real-world visual shifts.
Robustness under realistic corruptions remains a challenge for the deployment of these methods in autonomous driving, robotics, and other safety-critical settings. RoCo-Spring jointly evaluates clean accuracy and corrupted robustness, targeting methods that remain accurate under camera noise, adverse weather conditions, blur, compression, and changes in illumination.
A quick view of the latest registered teams and live public benchmark scores. Registered teams without scores remain visible on the full leaderboard.
Snapshot updated
Spring provides high-resolution stereo video with left and right images at 1920x1080 px, together with dense scene-flow ground truth at 4x super-resolution. It includes bidirectional disparities for stereo and depth, disparity changes for Scene Flow, and forward/backward Optical Flow for both left and right views. This makes Spring a unified high-detail benchmark for Optical Flow, Stereo Matching, and Scene Flow.
RobustSpring extends Spring into a robustness benchmark with 20 realistic image corruptions, including blur, color changes, noise, quality degradations, and weather effects. The corruptions are applied to stereo video data and are integrated consistently over time, across stereo views, and with depth where applicable, enabling controlled robustness evaluation for Optical Flow, Stereo Matching, and Scene Flow.
Estimate a dense 2D displacement field between consecutive frames, assigning each visible pixel a horizontal and vertical motion vector.
Estimate a dense disparity map from a rectified stereo image pair, assigning each pixel the horizontal offset to its corresponding point.
Estimate dense 3D motion from stereo image sequences, combining per-pixel geometry and temporal motion into a single correspondence task.
Submit a rigorous analysis of robustness in dense correspondence, focusing on failure modes, method behavior, metrics, or evaluation design.
| Date | Milestone |
|---|---|
| July 2, 2026 | Website launch |
| July 13, 2026 | Development leaderboard opens for all quantitative tasks |
| September 15, 2026 | 4–6 page workshop paper submission deadline |
| September 29, 2026 | Workshop paper author notification |
| September 30, 2026 | Final quantitative submission deadline |
| October 7, 2026 | Camera-ready paper, code, and reproducibility package deadline |
| October 15, 2026 | Final evaluation, reproducibility checks, and award shortlisting |
| October 31, 2026 | Winners notified and workshop program finalized |
| December 11 or 12, 2026 | In-person NeurIPS Competition Track workshop |
All dates are tentative and subject to change.
Princeton University · Princeton Vision & Learning Lab
Jia Deng is Professor of Computer Science at Princeton University and Director of the Princeton Vision & Learning Lab. He brings a uniquely relevant perspective to RoCo-Spring: his work spans benchmark-driven computer vision, large-scale datasets such as ImageNet[1], and influential dense correspondence architectures across multiple tasks, including RAFT for Optical Flow[2], RAFT-Stereo for Stereo Matching[3], and SEA-RAFT for efficient and accurate Optical Flow[4]. His keynote will connect the evolution of correspondence methods and benchmark design with the central question of the challenge: how to turn clean benchmark progress into robust Optical Flow, Stereo Matching, and Scene Flow methods that remain reliable under realistic visual shifts.
Qualcomm AI Research
Fatih Porikli is an IEEE Fellow and Vice President of Technology at Qualcomm AI Research. His expertise connects robust visual perception, video understanding, low-level vision, and real-world deployment. His prior work includes scene-flow estimation under degraded video conditions, including simultaneous stereo video deblurring and Scene Flow estimation[5], joint stereo video deblurring, scene flow estimation, and moving-object segmentation[6], and adversarial robustness for vision systems[7]. His talk will provide an industry-facing and deployment-oriented perspective on robust dense correspondence.
| Time | Session | Format |
|---|---|---|
| 08:15–08:25 | Opening and challenge overview | Organizers |
| 08:25–09:10 | Keynote: Jia Deng | 35 min talk + 10 min discussion |
| 09:10–09:55 | Keynote: Fatih Porikli | 35 min talk + 10 min discussion |
| 09:55–10:35 | Optical Flow track talks | 2 talks, 15+5 min each |
| 10:35–11:05 | Coffee break | 30 min |
| 11:05–11:45 | Stereo Matching track talks | 2 talks, 15+5 min each |
| 11:45–12:25 | Scene Flow track talks | 2 talks, 15+5 min each |
| 12:25–13:25 | Lunch | 60 min |
| 13:00–15:00 | Poster session | 2 hours, overlaps with lunch |
| 15:00–15:30 | Coffee break | 30 min |
| 15:30–16:10 | Exploration Track talks | 2 talks, 15+5 min each |
| 16:10–16:50 | Panel and audience forum | Organizers, speakers, teams |
| 16:50–17:00 | Awards and closing remarks | Organizers |
Tentative one-day workshop schedule, 8:15 to 17:00.
This event is supported by the SFB-TRR 161 Quantitative Methods for Visual Computing.
Shashank Agnihotri and Margret Keuper acknowledge funding by the DFG Research Unit 5336 – Learning to Sense (L2S). Margret Keuper acknowledges funding by BMFTR project TrackOpt (01IS24074A-D). Andres Bruhn and Victor Oei acknowledge funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 251654672 – TRR 161: Quantitative Methods for Visual Computing (B04, A07). Katrin Bauer and Andres Bruhn acknowledge funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 533085500 – Robust Optical Flow. Katrin Bauer and Victor Oei acknowledge support from the International Max Planck Research School for Intelligent Systems (IMPRS-IS).
Challenge email: roco-spring-org@googlegroups.com
GitHub Starter Kit: hmorimitsu/roco-spring-devkit
