Near-lossless Point Cloud Geometry Compression Based on Adaptive Residual Compensation

Abstract

Point cloud compression (PCC) is a crucial enabler for immersive multimedia applications since point cloud is one of the most primitive forms for representing 3D scenes and objects. Recently, some approaches are proposed to improve the average reconstruction quality of octree-based Geometry-based Point Cloud Compression (G-PCC). However, it is noticed that these approaches suffer considerable loss in terms of point-to-point (D1) Hausdorff distance when compared to G-PCC (octree). Here we introduce a near-lossless point cloud geometry compression method based on adaptive residual compensation by adding and removing points with large errors. It allows controlling of D1 Hausdorff (D1h) distance and maintains a great improvement in average reconstruction performance over G-PCC. Experimental results verify the effectiveness of our method, where our method achieves an average of 78.5% D1 and 11.4% D1h Bjontegaard-delta bitrate savings over the octree-based G-PCC on solid point clouds of the MPEG Cat1A dataset.