LOSSY LIDAR POINT CLOUD COMPRESSION VIA CYLINDRICAL 3D CONVOLUTION NETWORKS

Compared with object-level and human-level point clouds, LiDAR point clouds have larger data scales and are more sparse, posing a challenge for the existing learning-based lossy compression scheme. In this paper, we resolve this issue by transforming the point cloud into a cylindrical coordinate system. In this way, we can better retain points close to the sensor with a high density while extending the receptive field of convolution in areas of low point density. Following cylindrical quantization, an autoencoder is utilized to progressively downsample voxels. The coordinates and latent features are compressed by G-PCC and hyperprior-based entropy encoding respectively. The results demonstrate that our approach performs better than PCGCv2. The visualization results also show that our algorithm can better retain the shape of objects. Ablation studies further prove the efficiency of the cylindrical coordinates. The code is publicly available at https://github.com/AirManH/cylindrical_pcc.