# A Fixation-based 360{\\deg} Benchmark Dataset for Salient Object Detection Source: [A Fixation-based 360{\\deg} Benchmark Dataset for Salient Object Detection]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:42.780Z Authors: Yi Zhang, Lu Zhang, Wassim Hamidouche, Olivier Deforges Published: 2020-01-22T11:16:39.000Z ## Abstract Fixation prediction (FP) in panoramic contents has been widely investigated along with the booming trend of virtual reality (VR) applications. However, another issue within the field of visual saliency, salient object detection (SOD), has been seldom explored in 360{\\deg} (or omnidirectional) images due to the lack of datasets representative of real scenes with pixel-level annotations. Toward this end, we collect 107 equirectangular panoramas with challenging scenes and multiple object classes. Based on the consistency between FP and explicit saliency judgements, we further manually annotate 1,165 salient objects over the collected images with precise masks under the guidance of real human eye fixation maps. Six state-of-the-art SOD models are then benchmarked on the proposed fixation-based 360{\\deg} image dataset (F-360iSOD), by applying a multiple cubic projection-based fine-tuning method. Experimental results show a limitation of the current methods when used for SOD in panoramic images, which indicates the proposed dataset is challenging. Key issues for 360{\\deg} SOD is also discussed. The proposed dataset is available at https://github.com/PanoAsh/F-360iSOD. ## 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. ## Original sources and implementation [Original paper]() [PDF]()