三维点云广泛应用于无人驾驶、实景三维等领域，然而复杂场景的海量点云对存储、处理和传输等带来极大挑战。提出一种基于多尺度特征和注意力机制的深度变分自编码点云几何信息压缩算法MSA-GPCC，通过加入多尺度模型提取特征、变分自编码器构建熵模型，继而结合尺度注意力模块和多尺度特征，实现基于熵编码的点云几何信息高码率、低失真压缩。在MPEG数据集上进行的实验表明，相比G-PCC算法和基于深度学习的D-PCC算法，MSA-GPCC算法在点间等比特率下平均质量增益分别提升7.72和4.91 dB，点到面等比特率下平均质量增益分别提升5.56和3.09 dB。
3D point clouds have extensive applications in the auto-drive, 3D real scene, and other fields. But complex scene requires massive point clouds to represent which brings great challenges to storage space, data processing and transmission bandwidth. A multi-scale attention point cloud geometry compression (MSA-GPCC) is proposed to compress point cloud data based on multi-scale features, attention mechanism, and variational auto-encoders (VAE). Experiments and analysis are carried out based on MPEG data sets. The results show that MSA-GPCC performs better than those of the traditional G-PCC and deep-learning-based D-PCC algorithms, D1 BD-PSNR is improved by 7.72 and 4.91 dB respectively, and D2 BD-PSNR is improved by 5.56 and 3.09 dB respectively.
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