Fractional Motion Estimation for Point Cloud Compression
Fractional Motion Estimation for Point Cloud Compression
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DOI：
10.1109/dcc52660.2022.00045
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发表时间：
2022-02
期刊：
影响因子：
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通讯作者：
Haoran Hong;Eduardo Pavez;Antonio Ortega;R. Watanabe;Keisuke Nonaka
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文献类型：
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作者：
Haoran Hong;Eduardo Pavez;Antonio Ortega;R. Watanabe;Keisuke Nonaka
受视频编码中分数像素运动的成功启发，我们探索了具有分数体素分辨率的运动估计的设计，用于压缩动态3D点云的颜色属性。我们提出的基于块的分数体素运动估计方案考虑了点云和视频之间的根本差异，即，体素在帧内和跨帧分布的不规则性。我们表明，运动补偿可以受益于更高的分辨率参考和更准确的位移提供的分数精度。我们提出的方案显着优于可比的方法，只使用整数运动。所提出的方案可以与使用诸如区域自适应图形傅立叶变换和区域自适应Haar变换的变换的最先进的系统相结合，并向其添加相当大的增益。
Motivated by the success of fractional pixel motion in video coding, we explore the design of motion estimation with fractional-voxel resolution for compression of color attributes of dynamic 3D point clouds. Our proposed block-based fractional-voxel motion estimation scheme takes into account the fundamental differences between point clouds and videos, i.e., the irregularity of the distribution of voxels within a frame and across frames. We show that motion compensation can benefit from the higher resolution reference and more accurate displacements provided by fractional precision. Our proposed scheme significantly outperforms comparable methods that only use integer motion. The proposed scheme can be combined with and add sizeable gains to state-of-the-art systems that use transforms such as Region Adaptive Graph Fourier Transform and Region Adaptive Haar Transform.