Spatial Visibility and Temporal Dynamics: Revolutionizing Field of View Prediction in Adaptive Point Cloud Video Streaming

TL;DR

This paper introduces a novel spatial visibility and object-aware graph model for predicting field of view in point cloud video streaming, improving accuracy and real-time performance by leveraging historical visibility data and spatial relationships.

Contribution

It reformulates FoV prediction from a cell visibility perspective and develops a graph model that enhances long-term visibility prediction accuracy in real-time.

Findings

Reduces prediction MSE loss by up to 50% compared to state-of-the-art.

Maintains real-time processing at over 30fps for large point cloud videos.

Improves long-term cell visibility prediction accuracy.

Abstract

Field-of-View (FoV) adaptive streaming significantly reduces bandwidth requirement of immersive point cloud video (PCV) by only transmitting visible points in a viewer's FoV. The traditional approaches often focus on trajectory-based 6 degree-of-freedom (6DoF) FoV predictions. The predicted FoV is then used to calculate point visibility. Such approaches do not explicitly consider video content's impact on viewer attention, and the conversion from FoV to point visibility is often error-prone and time-consuming. We reformulate the PCV FoV prediction problem from the cell visibility perspective, allowing for precise decision-making regarding the transmission of 3D data at the cell level based on the predicted visibility distribution. We develop a novel spatial visibility and object-aware graph model that leverages the historical 3D visibility data and incorporates spatial perception, neighboring cell correlation, and occlusion information to predict the cell visibility in the future. Our model significantly improves the long-term cell visibility prediction, reducing the prediction MSE loss by up to 50% compared to the state-of-the-art models while maintaining real-time performance (more than 30fps) for point cloud videos with over 1 million points.