Multi-grained Point Cloud Geometry Compression via Dual-model Prediction with Extended Octree

Abstract

The state-of-the-art G-PCC (geometry-based point cloud compression) (Octree) is the fine-grained approach, which uses the octree to partition point clouds into voxels and predicts them based on neighbor occupancy in narrower spaces. However, G-PCC (Octree) is less effective at compressing dense point clouds than multi-grained approaches (such as G-PCC (Trisoup)), which exploit the continuous point distribution in nodes partitioned by the pruned octree over larger spaces. Therefore, we propose a lossy multi-grained compression with extended octree and dual-model prediction. The extended octree, where each partitioned node contains intra-block and extra-block points, is applied to address poor prediction (such as overfitting) at the node edges of the octree partition. For the points of each multi-grained node, dual-model prediction fits surfaces and projects residuals onto the surfaces, reducing projection residuals for efficient 2D compression and fitting complexity. In addition, a hybrid DWT-DCT transform for 2D projection residuals mitigates the resolution degradation of DWT and the blocking effect of DCT during high compression. Experimental results demonstrate the superior performance of our method over advanced G-PCC (Octree), achieving BD-rate gains of 55.9% and 45.3% for point-to-point (D1) and point-to-plane (D2) distortions, respectively. Our approach also outperforms G-PCC (Octree) and G-PCC (Trisoup) in subjective evaluation.