Enhanced Intra Prediction Scheme in Point Cloud Attribute Compression 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, videobased point cloud compression (VPCC) and geometrybased point cloud compression (GPCC). As an essential process in GPCC, Knearest neighbors (KNN) algorithm is adopted to perform intra prediction, which only considering distancebased local similarity but neglecting the overall geometric distribution of the neighbor set. In this paper, we propose an enhanced intra prediction scheme based on pointcloud geometric distribution. The centroidbased criterion is introduced to measure the uniformity of spatial distribution of predictive reference points. Our scheme is implemented in GPCC reference software. Experimental results demonstrate that our proposed method can optimize the selection of predictors, which leads to better ratedistortion (RD) performance than the GPCC anchor on point cloud datasets under the common test conditions (CTC).

IntraFrame Compression of Point Cloud Geometry using Boolean Decomposition Point clouds are one of the most promising technologies for 3D content representation. Point clouds are a set of points described by their geometry information and additional attributes, of which colour is the most typical. When transmitting a point cloud, both geometry, colour and temporal information need to be transmitted, thus making compression efficiency key in exploiting this technology. In this paper, we propose a lossless intra coder of the geometry information of voxelized point clouds. Instead of using the popular octree approach, we use wellknown techniques for coding of bilevel images, as well as a novel boolean decomposition method to further exploit the redundancy within the geometry. Results show that the proposed method outperforms other recent coders on the sequences tested.