# Saliency Prediction for Omnidirectional Images Considering Optimization on Sphere Domain

arXiv: https://arxiv.org/abs/1903.01380 · HTML: https://mlsanity.com/paper/1903.01380/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2019.

## Built on by (most cited first, 2 of 2)

- [A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision](https://mlsanity.com/paper/2502.10444/) (2025)
- [Spherical Convolution empowered FoV Prediction in 360-degree Video Multicast with Limited FoV Feedback](https://mlsanity.com/paper/2201.12525/) (2022)

## Abstract (arXiv)

There are several formats to describe the omnidirectional images. Among them, equirectangular projection (ERP), represented as 2D image, is the most widely used format. There exist many outstanding methods capable of well predicting the saliency maps for the conventional 2D images. But these works cannot be directly extended to predict the saliency map of the ERP image, since the content on ERP is not for direct display. Instead, the viewport image on demand is generated after converting the ERP image to the sphere domain, followed by rectilinear projection. In this paper, we propose a model to predict the saliency maps of the ERP images using existing saliency predictors for the 2D image. Some pre-processing and post-processing are used to manage the problem mentioned above. In particular, a smoothing based optimization is realized on the sphere domain. A public dataset of omnidirectional images is used to perform all the experiments and competitive results are achieved.