UGAE:G-PCC压缩点云的统一几何和属性增强
概括
本研究引入了一个统一的几何学和属性增强 (UGAE) 框架,以改进损失点云压缩. UGAE显著提高了几何和属性质量,优于现有的方法,降低了存储和传输成本.
科学领域:
- 计算机视觉 计算机视觉
- 3D数据处理 3D数据处理
- 信号压缩 压缩信号
背景情况:
- 点云的损耗压缩对于降低存储和传输成本至关重要.
- 现有的方法在几何结构和属性信息中遭受不可逆转的扭曲.
- 在属性压缩过程中,高频细节通常会丢失.
研究的目的:
- 提出统一的几何和属性增强 (UGAE) 框架,以解决损失点云压缩中的扭曲问题.
- 为了重建高精度的几何形状,并有效地保存属性细节.
- 为了提高压缩点云的整体感知质量.
主要方法:
- 该UGAE框架包括三个组成部分:几何后增强 (PoGE),属性前增强 (PAE) 和属性后增强 (PoAE).
- PoGE使用基于变压器的稀疏卷积U-Net进行精确的几何重建,通过预测voxel占用概率.
- PAE采用了增强的几何导向再染色策略,并采用了细节感知K-最近邻居 (DA-KNN) 方法来准确地再染色.
- PoAE使用一个属性剩余预测网络,用于属性增强的加权平均平方误差 (W-MSE) 损失.
主要成果:
- 在基准数据集 (8iVFB,Owlii,MVUB) 上,UGAE显著优于现有的方法.
- 与TMC13v29.29.相比,实现了BD-PSNR平均增益9.98dB和-90.54%的BD比特率,用于几何.
- 实现了3.34 dB的BD-PSNR改进和 -55.53%的BD比特率的属性.
- 在感知质量方面表现出显著的改善.
结论:
- 拟议的UGAE框架有效地减轻了损失点云压缩中的扭曲.
- 在几何学和属性增强方面,UGAE提供了卓越的性能,从而导致更高质量的点云数据.
- 该框架为高效和高可靠的点云存储和传输提供了一个有前途的解决方案.
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