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GRNet:使用辅助密度信号的G-PCC压缩点云的几何恢复
IEEE transactions on visualization and computer graphics
|November 27, 2023
概括
几何恢复网络 (GRNet) 通过解决点消失和位移,解决基于几何的点云压缩 (G-PCC) 的损失扭曲. 这种方法显著提高了点云质量,并优于现有的方法.
科学领域:
- 计算机视觉 计算机视觉
- 几何深度学习 几何深度学习
- 数据压缩数据压缩
背景情况:
- 基于丢失几何学的点云压缩 (G-PCC) 降低了点云几何,影响了重建质量和下游任务,如分类.
- G-PCC扭曲源于点消失和点位移,受原始点云密度的影响.
研究的目的:
- 提出GRNet,这是一个用于大规模G-PCC压缩点云的几何恢复的新型网络.
- 解决G-PCC造成的点消失和位移的双重挑战.
主要方法:
- GRNet采用两种专门的模型:用于点消失的坐标扩展和用于点位移的坐标精细化.
- 一个特征分析模块与基于kNN的变压器融合了多尺度信息,用于适应性邻里几何表征.
- 辅助密度信息指导恢复策略的选择.
主要成果:
- GRNet显著提高了G-PCC的 performance. 这是一个非常好的解决方案.
- 该方法在不同类型的点云 (固体,密集,稀疏) 中显示出优异的定量和质量结果.
- 与现有的基于学习的方法相比,GRNet实现了更快的处理速度,并使用了较小的模型.
结论:
- GRNet有效地恢复了G-PCC压缩点云中的几何,减轻了质量退化.
- 提出的方法为点云几何恢复提供了一个高效和有效的解决方案,适合实际应用.
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