Geometry Compression Using Normal Uncertainty
| dc.contributor.author | Káčereková, Zuzana | en_US |
| dc.contributor.author | Hácha, Filip | en_US |
| dc.contributor.author | Váša, Libor | en_US |
| dc.contributor.editor | Sheng, Yun | en_US |
| dc.contributor.editor | Slingsby, Aidan | en_US |
| dc.date.accessioned | 2025-09-09T05:12:17Z | |
| dc.date.available | 2025-09-09T05:12:17Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | 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-allocating more bits to perceptually sensitive regions and fewer where differences go unnoticed. This requires identifying surface regions where distortion would be most noticeable, a task often guided by perceptual metrics that approximate human judgment. Existing perceptual-driven progressive compression methods rely on these metrics to steer refinement, but doing so typically incurs additional data overhead to specify where each refinement occurs. We propose a novel 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. Compared to a baseline of Edgebreaker with weighed parallelogram prediction, our method produces reconstructions ranked higher by several established perceptual metrics. However, its high computational cost currently limits practical deployment. | en_US |
| dc.description.sectionheaders | Geometry, Rendering, Animation | |
| dc.description.seriesinformation | Computer Graphics and Visual Computing (CGVC) | |
| dc.identifier.doi | 10.2312/cgvc.20251211 | |
| dc.identifier.isbn | 978-3-03868-293-6 | |
| dc.identifier.pages | 9 pages | |
| dc.identifier.uri | https://doi.org/10.2312/cgvc.20251211 | |
| dc.identifier.uri | https://diglib.eg.org/handle/10.2312/cgvc20251211 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.rights | Attribution 4.0 International License | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | CCS Concepts: Computing methodologies → Mesh models; Image compression | |
| dc.subject | Computing methodologies → Mesh models | |
| dc.subject | Image compression | |
| dc.title | Geometry Compression Using Normal Uncertainty | en_US |
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