OctGLP-Net: Learning Octree-Structured Context Entropy Model With Global–Local Perception for Point Cloud Geometry Compression

The insufficient exploitation of spatial correlations among octree node context and feature interactions across spatial and channel dimensions limits the reconstruction performance of current point cloud geometry compression (PCGC) models. To solve these issues, this paper presents an octree-structured context entropy model OctGLP-Net with global-local perception, which mainly consists of a Local Spatial Perception (LocSP) module, a Scaled-cosine Attention based Spatial Interaction (SASI) module, and a Locally-enhanced Feed-forward Spatial and Channel Interaction (LFSCI) module. First, we propose the LocSP to extract local context information from octree nodes. Then, we introduce the SASI to fully exploit the spatial correlations among nodes. Finally, to effectively interact local features across spatial and channel dimensions, we devise a LFSCI network by employing depth-wise and point-wise convolutions to realize fine-grained local feature extraction from octree nodes. Experimental results on both sparse LiDAR and dense object benchmark datasets demonstrate that our method achieves state-of-the-art lossy/lossless compression performance. Our method obtains higher reconstruction quality (D1/D2 PSNR) and smaller chamfer distance (CD) at similar bits per point (BPP) on the SemanticKITTI, nuScenes, and LiDAR-CS datasets, and lower bitrate on the Owlii, 8iVFB and MVUB datasets. Significantly, the proposed OctGLP-Net also exhibits strong generalization abilities when applied to unseen nuScenes and LiDAR-CS datasets. In addition, downstream object detection task on the nuScenes dataset with different compression precisions further demonstrate the superiority and robustness of our method.