Point cloud compression of deep learning based on multi-scale feature and attention mechanism
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.
point cloud compressiondeep learningattention mechanismvariational auto-encodersmulti-scale feature
万方数据
维普
