Decoupled Heightfield Tessellation: Eliminating Adjacency Dependency for High-Throughput Spatial Computing

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Modern terrain rendering frameworks often struggle with the inherent trade-off between geometric continuity and hardware utilization. State-of-the-art watertight tessellation methods typically rely on explicit adjacency management and multi-pass synchronization, which severely restrict GPU parallelism and induce significant performance spikes during rapid camera transitions. In this paper, we propose a high-throughput, decoupled terrain rendering architecture that ensures visual continuity without the need for horizontal dependency tracking. Our framework leverages a linearized binary tree maintained within a 1D Unordered Access View (UAV), enabling each GPU thread to perform refinement and geometric reconstruction in total isolation. To resolve T-junction artifacts, we introduce an adjacency-blind directional stitching mechanism that utilizes localized hierarchical flags and per-edge error metrics to perform deterministic geometric repair in a single execution pass. Experimental results demonstrate that the proposed method achieves a stable rendering throughput of over 170 FPS on a large-scale 8192×8192 dataset. Notably, our approach provides a 14% improvement in frame-rate stability during instantaneous camera teleportation compared to existing watertight frameworks, while simultaneously reducing the average triangle count by 21% through optimized boundary repair. These results confirm that our decoupled pipeline offers a robust and scalable solution for real-time spatial computing and high-performance simulation environments.