Enhanced Intra Prediction Scheme in Point Cloud Attribute Compression

Abstract

3D point cloud compression (PCC) has been an attractive field with increasing applications in recent years. Moving Picture Experts Group (MPEG) is building an open standard for point cloud compression, consisting of two solutions, video-based point cloud compression (V-PCC) and geometry-based point cloud compression (G-PCC). As an essential process in G-PCC, K-nearest neighbors (KNN) algorithm is adopted to perform intra prediction, which only considering distance-based local similarity but neglecting the overall geometric distribution of the neighbor set. In this paper, we propose an enhanced intra prediction scheme based on point-cloud geometric distribution. The centroid-based criterion is introduced to measure the uniformity of spatial distribution of predictive reference points. Our scheme is implemented in G-PCC reference software. Experimental results demonstrate that our proposed method can optimize the selection of predictors, which leads to better rate-distortion (R-D) performance than the G-PCC anchor on point cloud datasets under the common test conditions (CTC).