论文标题
4DAC：动态点云的学习属性压缩
4DAC: Learning Attribute Compression for Dynamic Point Clouds
论文作者
论文摘要
随着3D数据采集设施的开发，获得的3D点云的尺度不断增加，对现有数据压缩技术构成了挑战。尽管在静态点云压缩中已经实现了有希望的性能，但是在点云序列中利用时间相关性以进行有效的动态点云压缩。在本文中，我们研究了动态点云的属性（例如，颜色）压缩，并呈现一个基于学习的框架，称为4DAC。为了减少数据中的时间冗余，我们首先使用深神经网络构建3D运动估计和运动补偿模块。然后，由运动补偿成分产生的属性残差由区域自适应分层变换编码为残余系数。此外，我们还提出了一个深层条件熵模型，以通过结合连续点云和运动估计/补偿模块的时间上下文来估计转换系数的概率分布。最后，数据流是通过预测分布的无损熵编码的。在几个公共数据集上进行的广泛实验证明了该方法的出色压缩性能。
With the development of the 3D data acquisition facilities, the increasing scale of acquired 3D point clouds poses a challenge to the existing data compression techniques. Although promising performance has been achieved in static point cloud compression, it remains under-explored and challenging to leverage temporal correlations within a point cloud sequence for effective dynamic point cloud compression. In this paper, we study the attribute (e.g., color) compression of dynamic point clouds and present a learning-based framework, termed 4DAC. To reduce temporal redundancy within data, we first build the 3D motion estimation and motion compensation modules with deep neural networks. Then, the attribute residuals produced by the motion compensation component are encoded by the region adaptive hierarchical transform into residual coefficients. In addition, we also propose a deep conditional entropy model to estimate the probability distribution of the transformed coefficients, by incorporating temporal context from consecutive point clouds and the motion estimation/compensation modules. Finally, the data stream is losslessly entropy coded with the predicted distribution. Extensive experiments on several public datasets demonstrate the superior compression performance of the proposed approach.
