# Distortion-adaptive Salient Object Detection in 360$^\\circ$ Omnidirectional Images

Source: [Distortion-adaptive Salient Object Detection in 360$^\\circ$ Omnidirectional Images](<https://www.sota2.com/research/paper/68fa66551e209131cea0f919>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:47:30.843Z

Authors: Jia Li, Jinming Su, Changqun Xia, Yonghong Tian

Published: 2019-09-11T08:33:11.000Z

## Abstract

Image-based salient object detection (SOD) has been extensively explored in the past decades. However, SOD on 360$^\\circ$ omnidirectional images is less studied owing to the lack of datasets with pixel-level annotations. Toward this end, this paper proposes a 360$^\\circ$ image-based SOD dataset that contains 500 high-resolution equirectangular images. We collect the representative equirectangular images from five mainstream 360$^\\circ$ video datasets and manually annotate all objects and regions over these images with precise masks with a free-viewpoint way. To the best of our knowledge, it is the first public available dataset for salient object detection on 360$^\\circ$ scenes. By observing this dataset, we find that distortion from projection, large-scale complex scene and small salient objects are the most prominent characteristics. Inspired by these foundings, this paper proposes a baseline model for SOD on equirectangular images. In the proposed approach, we construct a distortion-adaptive module to deal with the distortion caused by the equirectangular projection. In addition, a multi-scale contextual integration block is introduced to perceive and distinguish the rich scenes and objects in omnidirectional scenes. The whole network is organized in a progressively manner with deep supervision. Experimental results show the proposed baseline approach outperforms the top-performanced state-of-the-art methods on 360$^\\circ$ SOD dataset. Moreover, benchmarking results of the proposed baseline approach and other methods on 360$^\\circ$ SOD dataset show the proposed dataset is very challenging, which also validate the usefulness of the proposed dataset and approach to boost the development of SOD on 360$^\\circ$ omnidirectional scenes.

## 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](<https://arxiv.org/abs/1909.04913>)

[PDF](<https://arxiv.org/pdf/1909.04913.pdf>)