Faster progressive geometry compression via normal uncertainty

doi:10.1016/j.cag.2026.104736

Faster progressive geometry compression via normal uncertainty

2026-08-01

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DOI：10.1016/j.cag.2026.104736

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Progressive compression of triangle mesh geometry typically exploits spatial coherence to reduce data size while preserving surface detail. In applications where lossy compression is permissible, an effective strategy is to align distortion with the limitations of human visual perception. Existing methods rely on perceptual metrics to steer refinement, but doing so typically incurs additional data overhead to specify where each refinement occurs. We propose a progressive geometry compression algorithm that leverages a perceptually informed model of normal uncertainty to predict where distortion is most likely to be noticeable. This enables the encoder to focus refinements in those regions without explicitly transmitting their locations at each step, thereby reducing overhead. While our initial approach produced reconstructions ranked higher by established perceptual metrics compared to a baseline of Edgebreaker with weighted parallelogram prediction, its high computational cost limited practical deployment. In this extended work, we address this bottleneck by introducing an Adaptive Quasi-Monte Carlo sampling method utilizing a Halton sequence and an early-stopping heuristic. This strategy substantially accelerates the compression pipeline and improves the overall perceptual quality. Furthermore, we explore a neural estimator with the potential to further speed up the normal uncertainty evaluation process. However, while this approach shows potential, further research is required to achieve sufficient accuracy and reliability for practical integration.

Keywords:

Progressive

Compression

Mesh

Geometry

Perception

Faster progressive geometry compression via normal uncertainty

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