Orhan, SemihBaştanlar, Yalın2021-11-062024-01-062021-11-062024-01-06202120221863-17031863-1711https://doi.org/10.1007/s11760-021-02003-3https://hdl.handle.net/11147/11368Omnidirectional cameras are capable of providing 360. field-of-view in a single shot. This comprehensive view makes them preferable for many computer vision applications. An omnidirectional view is generally represented as a panoramic image with equirectangular projection, which suffers from distortions. Thus, standard camera approaches should be mathematically modified to be used effectively with panoramic images. In this work, we built a semantic segmentation CNN model that handles distortions in panoramic images using equirectangular convolutions. The proposed model, we call it UNet-equiconv, outperforms an equivalent CNN model with standard convolutions. To the best of our knowledge, ours is the first work on the semantic segmentation of real outdoor panoramic images. Experiment results reveal that using a distortion-aware CNN with equirectangular convolution increases the semantic segmentation performance (4% increase in mIoU). We also released a pixel-level annotated outdoor panoramic image dataset which can be used for various computer vision applications such as autonomous driving and visual localization. Source code of the project and the dataset were made available at the project page (https://github.com/semihorhan/semseg-outdoor-pano). © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.eninfo:eu-repo/semantics/closedAccessSemantic segmentationComputer vision applicationsPanoramic imagesConvolutional neural networksOmnidirectional visionPanoramic imagesSemantic segmentationCamerasComputer visionConvolutionSemanticsAutonomous drivingOmni-directional viewOmnidirectional camerasPanoramic imagesSemantic segmentationStandard camerasVisual localizationImage segmentationOmnidirectional visionConvolutional neural networksArticle2-s2.0-8511253751210.1007/s11760-021-02003-3