QUEST-PCC: A Reference Dataset for Content-Aware V-PCC Streaming and Compression

Volumetric media streaming is a key enabler of immersive 6DoF applications such as virtual reality and interactive telepresence. Dynamic point clouds, standardized by MPEG through Video-based Point Cloud Compression (V-PCC), offer an efficient representation for such content. However, existing datasets and quality evaluation studies are largely human-centric and fail to capture the heterogeneity of real-world volumetric scenes in terms of object category, motion dynamics, and geometric complexity, limiting content-aware systems analysis. In this paper, we introduce QUEST-PCC1, an open-source reference dataset for objective quality and rate-distortion analysis of heterogeneous dynamic point clouds encoded using the MPEG V-PCC standard. QUEST-PCC spans six object categories, including articulated, rigid, and non-human objects, and covers a wide range of motion and structural complexity. The dataset provides reconstructed point clouds, compressed V-PCC bitstreams, and standardized per-frame MPEG PCC quality metrics across multiple rate levels, yielding content-dependent bitrate ladders that span from sub-1 Mbps to approximately 80 Mbps. Through systematic sequence-level and category-level analysis, we show that encoding bitrate and reconstruction quality are governed primarily by dynamic surface topology and structural complexity rather than point count alone, revealing content-dependent rate-distortion behavior that is not captured by existing human-centric datasets and is critical for content-aware compression and adaptive volumetric streaming.