ScanPCGC: Learning-Based Lossless Point Cloud Geometry Compression using Sequential Slice Representation

• Published in: ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 8386 - 8390
• Main Authors: Deng, Jiangwei, An, Yuhao, Li, Thomas H., Liu, Shan, Li, Ge
• Format: Conference Proceeding
• Language: English
• Published: IEEE 14.04.2024
• Subjects: Autoregressive Context; Entropy; Entropy Model; Geometry; Grouped Context; Point cloud compression; Point Cloud Geometry Compression; Sequential Slice Representation; Solid modeling; Three-dimensional displays; Transform coding; Transportation
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP48485.2024.10447944

The efficient storage and transportation requirements of point clouds promote the development of point cloud compression algorithms. In this paper, we develop a novel point cloud geometry compression using sequential slice representation. Unlike the limited contexts in conventional codecs and other voxel-based works, sufficient contexts are provided by the previous slices, enabling more accurate modeling of the current slice distribution. To reduce the sparsity of the point cloud, we further divide each slice into patches and reorganize non-empty patches along with contexts fed into the conditional entropy model. The 3D convolution-based entropy model with residual structure is designed to exploit sufficient contexts and estimate a probability distribution of the voxels in non-empty patches. In addition to auto-regressive context, we provide a grouped context to address the serial decoding issue. Experimental results on object point cloud datasets (e.g., MPEG 8i, MVUB) demonstrate that our approaches outperform MPEG G-PCC and competitive learning-based methods.