Local Texture and Geometry Descriptors for Fast Block-Based Motion Estimation of Dynamic Voxelized Point Clouds
Local Texture and Geometry Descriptors for Fast Block-Based Motion Estimation of Dynamic Voxelized Point Clouds
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DOI：
10.1109/icip.2019.8803690
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发表时间：
2019-09
期刊：
影响因子：
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通讯作者：
Camilo C. Dorea;E. Hung;R. Queiroz
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文献类型：
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作者：
Camilo C. Dorea;E. Hung;R. Queiroz
动态点云分析或压缩中的运动估计是计算密集的过程，通常涉及大的搜索空间和通常复杂的体素匹配函数。我们提出了一个扩展和改进以前的工作，以加快基于块的运动估计之间的时间相邻的点云。我们引入本地的，或基于块的，纹理描述符作为补充体素几何描述。描述符被组织在可以被有效地计算和存储的占用图中。通过查阅地图，点云运动估计器可以显著减小其搜索空间，同时将预测失真维持在类似的质量水平。建议的基于纹理的占用地图提供了显着的加速，平均为26.9%的测试数据集，相对于以前的工作。
Motion estimation in dynamic point cloud analysis or compression is a computationally intensive procedure generally involving a large search space and often complex voxel matching functions. We present an extension and improvement on prior work to speed up block-based motion estimation between temporally adjacent point clouds. We introduce local, or block-based, texture descriptors as a complement to voxel geometry description. Descriptors are organized in an occupancy map which may be efficiently computed and stored. By consulting the map, a point cloud motion estimator may significantly reduce its search space while maintaining prediction distortion at similar quality levels. The proposed texture-based occupancy maps provide significant speedup, an average of 26.9% for the tested data set, with respect to prior work.