Logistic Regression Guided Coding of Single Child Mode for Point Cloud Geometry Compression

Geometry coding in geometry-based point cloud compression (G-PCC) is octree-structured, including a bitwise occupancy mode for a general case, and a single child mode for a node containing a single occupied child node. However, the current usage of the single child mode is limited because of the strict eligibility determination based on neighboring nodes. Context modeling is also missing for entropy coding of the coordinate index of the single occupied child node relative to the node. Guided by logistic regression (LR), this paper first proposes an algorithm to determine the eligibility of a node for the single child mode. Without resorting to the occupancy of the neighboring nodes, the proposed algorithm provides more opportunities for employing the single child mode. In addition, LR is also used in predicting the relative coordinate index of the single occupied child node. Based on the analysis of predicted results, we model contexts for the entropy coding of the single child mode. Experiments reveal that the proposed method improves the existing single child mode in G-PCC with overall coding gain in terms of bit per input point (bpip). Besides, the proposed method also saves coding time.