# Spherical Convolution empowered FoV Prediction in 360-degree Video Multicast with Limited FoV Feedback

arXiv: https://arxiv.org/abs/2201.12525 · HTML: https://mlsanity.com/paper/2201.12525/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 57 references that are in the map.
- First posted on arXiv 29 Jan 2022.

## Builds on (most cited first, 15 of 15)

- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Spherical CNNs](https://mlsanity.com/paper/1801.10130/) (2018)
- [What do different evaluation metrics tell us about saliency models?](https://mlsanity.com/paper/1604.03605/) (2016)
- [Revisiting Video Saliency: A Large-scale Benchmark and a New Model](https://mlsanity.com/paper/1801.07424/) (2018)
- [Predicting Goal-directed Human Attention Using Inverse Reinforcement Learning](https://mlsanity.com/paper/2005.14310/) (2020)
- [How is Gaze Influenced by Image Transformations? Dataset and Model](https://mlsanity.com/paper/1905.06803/) (2019)
- [Learning Spherical Convolution for Fast Features from 360deg Imagery](https://mlsanity.com/paper/1708.00919/) (2017)
- [Predicting Video Saliency with Object-to-Motion CNN and Two-layer Convolutional LSTM](https://mlsanity.com/paper/1709.06316/) (2017)
- [Convolutions on Spherical Images](https://mlsanity.com/paper/1905.08409/) (2019)
- [Deep Learning for Content-based Personalized Viewport Prediction of 360-Degree VR Videos](https://mlsanity.com/paper/2003.00429/) (2020)
- [Power-Efficient Wireless Streaming of Multi-Quality Tiled 360 VR Video in MIMO-OFDMA Systems](https://mlsanity.com/paper/2105.09865/) (2021)
- [AI on the Edge: Rethinking AI-based IoT Applications Using Specialized Edge Architectures](https://mlsanity.com/paper/2003.12488/) (2020)
- [Saliency Prediction for Omnidirectional Images Considering Optimization on Sphere Domain](https://mlsanity.com/paper/1903.01380/) (2019)
- [PARIMA: Viewport Adaptive 360-Degree Video Streaming](https://mlsanity.com/paper/2103.00981/) (2021)
- [QoE-driven Coupled Uplink and Downlink Rate Adaptation for 360-degree Video Live Streaming](https://mlsanity.com/paper/2001.03536/) (2020)

## Built on by (most cited first, 1 of 1)

- [SalFormer360: a transformer-based saliency estimation model for 360-degree videos](https://mlsanity.com/paper/2602.04584/) (2026)

## Abstract (arXiv)

Field of view (FoV) prediction is critical in 360-degree video multicast, which is a key component of the emerging Virtual Reality (VR) and Augmented Reality (AR) applications. Most of the current prediction methods combining saliency detection and FoV information neither take into account that the distortion of projected 360-degree videos can invalidate the weight sharing of traditional convolutional networks, nor do they adequately consider the difficulty of obtaining complete multi-user FoV information, which degrades the prediction performance. This paper proposes a spherical convolution-empowered FoV prediction method, which is a multi-source prediction framework combining salient features extracted from 360-degree video with limited FoV feedback information. A spherical convolution neural network (CNN) is used instead of a traditional two-dimensional CNN to eliminate the problem of weight sharing failure caused by video projection distortion. Specifically, salient spatial-temporal features are extracted through a spherical convolution-based saliency detection model, after which the limited feedback FoV information is represented as a time-series model based on a spherical convolution-empowered gated recurrent unit network. Finally, the extracted salient video features are combined to predict future user FoVs. The experimental results show that the performance of the proposed method is better than other prediction methods.