MazeLab: A Large-Scale Dynamic Volumetric Point Cloud Video Dataset With User Behavior Traces

Point cloud video datasets enriched with user behavior traces are critical for advancing tile-based HTTP adaptive streaming (HAS) systems, particularly those reliant on large training data for player modules like Field of View (FoV) prediction. This paper presents MazeLab, a dynamic volumetric video dataset comprising a feature-rich point cloud representation of a large maze environment. The dataset captures navigation traces from 15 participants interacting with 15 distinct maze variants, categorized into seven classes designed to elicit specific behavioral characteristics such as navigation patterns, attention hotspots, and interaction dynamics. By incorporating diverse environmental designs, each targeting unique user responses, MazeLab provides a comprehensive behavioral repository, enabling robust testing and optimization of HAS-driven volumetric video systems in complex scenarios. The dataset is publicly available here.