论文标题
3DAC：学习点云的学习属性压缩
3DAC: Learning Attribute Compression for Point Clouds
论文作者
论文摘要
我们研究了大规模非结构化3D点云的属性压缩问题。通过深入探索不同编码步骤和不同属性通道之间的关系，我们引入了一个以3DAC为3DAC的深度压缩网络，以明确压缩3D点云的属性并减少本文中的存储使用情况。具体而言，诸如颜色和反射率之类的点云属性首先转换为转换系数。然后，我们提出了一个深度熵模型，通过考虑隐藏在属性变换和先前的编码属性中的信息来对这些系数的概率进行建模。最后，估计的概率用于进一步压缩这些转换系数到最终属性bitstream。在室内和室外大规模开放点云数据集上进行的广泛实验，包括扫描仪和semantickitti，证明了拟议的3DAC的卓越压缩率和重建质量。
We study the problem of attribute compression for large-scale unstructured 3D point clouds. Through an in-depth exploration of the relationships between different encoding steps and different attribute channels, we introduce a deep compression network, termed 3DAC, to explicitly compress the attributes of 3D point clouds and reduce storage usage in this paper. Specifically, the point cloud attributes such as color and reflectance are firstly converted to transform coefficients. We then propose a deep entropy model to model the probabilities of these coefficients by considering information hidden in attribute transforms and previous encoded attributes. Finally, the estimated probabilities are used to further compress these transform coefficients to a final attributes bitstream. Extensive experiments conducted on both indoor and outdoor large-scale open point cloud datasets, including ScanNet and SemanticKITTI, demonstrated the superior compression rates and reconstruction quality of the proposed 3DAC.
