LL-Sparse: Low-Latency 6-DoF Field of View Prediction

Field of view (FoV) prediction is crucial for optimizing 6-DoF dynamic point cloud-based volumetric video (PCV) streaming. By accurately predicting which tiles fall within the viewer's region of interest, FoV prediction enables adaptive bitrate (ABR) algorithms to allocate higher bitrates to likely viewed tiles while assigning lower bitrates to less critical areas. This improves bandwidth efficiency and enhances the quality of experience (QoE) by aligning bitrate allocation with the viewer's focus. However, current 6-DoF salience-aware FoV prediction models face challenges related to high latency, computational costs, and a lack of complex datasets with detailed FoV traces, hindering the development of more effective real-time predictors. To address these challenges, we propose the LL-Sparse family, a suite of three solutions for direct tile salience score prediction: LL-Adapter, an extension of HMD-trajectory-based (HTB) models, such as GRUs, tailored for tile scoring; LL-PointNet, which integrates a GRU with PointNet to enhance salience-aware prediction; and LL-SparseConv, a scalable variant of LL-PointNet that employs sparse convolution in place of PointNet, serving as a proof of concept. These models strike a balance between practical performance and theoretical advancements in tile salience prediction. Furthermore, we introduce the MazeLab dataset, a novel, large-scale dynamic point cloud dataset that mimics real-world PCV scenarios to effectively benchmark FoV prediction models. Experimental results highlight the LL-Sparse family's exceptional scalability, reduced latency, and enhanced accuracy, establishing it as a promising solution for efficient real-time volumetric media applications.