# SalNet360: Saliency Maps for omni-directional images with CNN

arXiv: https://arxiv.org/abs/1709.06505 · HTML: https://mlsanity.com/paper/1709.06505/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2017.

## Built on by (most cited first, 21 of 21)

- [Geometric Deep Learning and Equivariant Neural Networks](https://mlsanity.com/paper/2105.13926/) (2021)
- [Deep Learning for Omnidirectional Vision: A Survey and New Perspectives](https://mlsanity.com/paper/2205.10468/) (2022)
- [Elite360D: Towards Efficient 360 Depth Estimation via Semantic- and Distance-Aware Bi-Projection Fusion](https://mlsanity.com/paper/2403.16376/) (2024)
- [A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision](https://mlsanity.com/paper/2502.10444/) (2025)
- [ScanGAN360: A Generative Model of Realistic Scanpaths for 360circ Images](https://mlsanity.com/paper/2103.13922/) (2021)
- [Multi-Projection Fusion and Refinement Network for Salient Object Detection in 360deg Omnidirectional Image](https://mlsanity.com/paper/2212.12378/) (2022)
- [OmniSplat: Taming Feed-Forward 3D Gaussian Splatting for Omnidirectional Images with Editable Capabilities](https://mlsanity.com/paper/2412.16604/) (2024)
- [PanoDepth: A Two-Stage Approach for Monocular Omnidirectional Depth Estimation](https://mlsanity.com/paper/2202.01323/) (2022)
- [Deep 360^circ Optical Flow Estimation Based on Multi-Projection Fusion](https://mlsanity.com/paper/2208.00776/) (2022)
- [Single-Shot Cuboids: Geodesics-based End-to-end Manhattan Aligned Layout Estimation from Spherical Panoramas](https://mlsanity.com/paper/2102.03939/) (2021)
- [OSLO: On-the-Sphere Learning for Omnidirectional images and its application to 360-degree image compression](https://mlsanity.com/paper/2107.09179/) (2021)
- [Interpolated SelectionConv for Spherical Images and Surfaces](https://mlsanity.com/paper/2210.10123/) (2022)
- [DiffGaze: A Diffusion Model for Continuous Gaze Sequence Generation on 360deg Images](https://mlsanity.com/paper/2403.17477/) (2024)
- [Multi-Scale Estimation for Omni-Directional Saliency Maps Using Learnable Equator Bias](https://mlsanity.com/paper/2309.08139/) (2023)
- [360VFI: A Dataset and Benchmark for Omnidirectional Video Frame Interpolation](https://mlsanity.com/paper/2407.14066/) (2024)
- [Saliency-based Multiple Region of Interest Detection from a Single 360deg image](https://mlsanity.com/paper/2209.03656/) (2022)
- [MRGAN360: Multi-stage Recurrent Generative Adversarial Network for 360 Degree Image Saliency Prediction](https://mlsanity.com/paper/2303.08525/) (2023)
- [GazeProphet: Software-Only Gaze Prediction for VR Foveated Rendering](https://mlsanity.com/paper/2508.13546/) (2025)
- [GazeProphetV2: Head-Movement-Based Gaze Prediction Enabling Efficient Foveated Rendering on Mobile VR](https://mlsanity.com/paper/2511.19988/) (2025)
- [Cross360: 360deg Monocular Depth Estimation via Cross Projections Across Scales](https://mlsanity.com/paper/2601.17271/) (2026)
- [SphereSOD: Geometry-Structure Coupled Learning for 360 Salient Object Detection](https://mlsanity.com/paper/2609.07571/) (2026)

## Abstract (arXiv)

The prediction of Visual Attention data from any kind of media is of valuable use to content creators and used to efficiently drive encoding algorithms. With the current trend in the Virtual Reality (VR) field, adapting known techniques to this new kind of media is starting to gain momentum. In this paper, we present an architectural extension to any Convolutional Neural Network (CNN) to fine-tune traditional 2D saliency prediction to Omnidirectional Images (ODIs) in an end-to-end manner. We show that each step in the proposed pipeline works towards making the generated saliency map more accurate with respect to ground truth data.