Introduction:

GPUs can encounter memory capacity constraints, which pose challenges for achieving real-time rendering performance when processing large 3D models that exceed available memory. State-of-the-art out-of-core rendering frameworks have leveraged Level of Detail (LOD) and frame-to-frame coherence data management techniques to optimize memory usage and minimize CPU-to-GPU data transfer costs. However, the size of view-dependently selected data may still exceed GPU memory capacity, and data transfer remains the most significant bottleneck in overall performance costs. To address these, we introduce a new GPU out-of-core rendering approach that includes a LOD selection method that takes into account both memory and coherence constraints and a parallel in-place GPU memory management algorithm that efficiently assembles the data of the current frame with GPU-resident data from the previous frame and transferred data. Our approach bounds memory usage and data transfer costs, prioritizes and schedules the transfer of essential data, incrementally refining the LOD over subsequent frames to converge toward the desired visual fidelity. Our parallel memory management algorithm consolidates frame-different and reusable data, dynamically reallocating GPU memory slots for efficient in-place operations. Hierarchical LOD representations remain a core component, and we emphasize their role in supporting adaptive data transfer and coherence management, characterized by a uniform depth and near-equal patch size at all levels. Our approach adapts seamlessly to scenarios with varying levels of coherence by balancing real-time performance with visual consistency. Experimental results demonstrate that our system achieves significant performance improvements, rendering scenes with billions of triangles in real-time, outperforming existing methods while maintaining consistent visual quality during dynamic interactions.