# A Cross-Season Correspondence Dataset for Robust Semantic Segmentation Source: [A Cross-Season Correspondence Dataset for Robust Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:44:30.600Z Authors: M{\\aa}ns Larsson, Erik Stenborg, Lars Hammarstrand, Torsten Sattler, Mark Pollefeys, Fredrik Kahl Published: 2019-03-16T13:01:23.000Z ## Abstract In this paper, we present a method to utilize 2D-2D point matches between images taken during different image conditions to train a convolutional neural network for semantic segmentation. Enforcing label consistency across the matches makes the final segmentation algorithm robust to seasonal changes. We describe how these 2D-2D matches can be generated with little human interaction by geometrically matching points from 3D models built from images. Two cross-season correspondence datasets are created providing 2D-2D matches across seasonal changes as well as from day to night. The datasets are made publicly available to facilitate further research. We show that adding the correspondences as extra supervision during training improves the segmentation performance of the convolutional neural network, making it more robust to seasonal changes and weather conditions. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Semantic Segmentation on RobotCar Correspondence (test)](): Semantic segmentation; RobotCar Correspondence (test); mIoU: 0.554. - [Semantic Segmentation on CMU Correspondence (test)](): Semantic segmentation; CMU Correspondence (test); mIoU: 79.3. ## Original sources and implementation [Original paper]() [PDF]()