RoCo-Spring: The Robust Correspondence Challenge

Dense correspondence has made remarkable progress on standard benchmarks, with modern optical flow, stereo matching, and scene flow methods achieving increasingly strong i.i.d. performance. However, this progress still does not translate reliably to real-world deployment, where camera noise, adverse weather, blur, compression, and illumination changes can substantially degrade performance in safety-critical settings such as autonomous driving, robotics, augmented reality, and scientific imaging. RoCo-Spring, short for Robust Correspondence on Spring, targets this gap between clean benchmark accuracy and robustness under realistic distribution shifts. The competition uses the Spring and RobustSpring benchmark because they provide a particularly well-suited foundation for this question: high-resolution, high-detail scenes; dense ground truth for optical flow, stereo, and scene flow; and paired clean and corrupted evaluations in which the corruptions remain time-, stereo-, and depth-consistent. This makes it possible to measure robustness without conflating it with unrelated changes in scene content, annotation quality, or benchmark protocol. Participants will compete on three quantitative leaderboards: optical flow, stereo matching, and scene flow, using a balanced score that jointly rewards low error on Spring and RobustSpring. Each entry will also submit a 4-6 page single-blind workshop paper that describes the method, training data, compute, and evidence behind the submission. A fourth exploration track will reward rigorous studies of why current methods fail or succeed under robustness evaluation. The starting kit will provide PTLFlow-based training, evaluation, packaging, and submission scripts, together with baseline results from Spring and RobustSpring. The expected outcome is a worldwide, reproducible effort to move dense correspondence beyond clean benchmark accuracy toward methods that remain accurate under realistic deployment conditions.