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.

3D Point Cloud Attribute Compression Based on Cylindrical Projection With the rapid development of 3D acquisition technologies, there are mounting interests in representing realworld objects as 3D point clouds. However, nonuniform point positions and abundant photometry attributes of point cloud (i.e., colors, reflectance and normal directions) result in massive data volume, leading to the heavy overload of transmission. To address this issue, we propose an attribute compression method for static 3D point cloud by taking advantage of 2D video coding technology. The attribute is projected onto 2D images which are generated from cylindrical surface for several times. We require consistent angle for each projection to guarantee the spatial distribution of multiple projected images is similar, then we combine these images into video sequence. 2D video codec is utilized to perform compression. In this way, we benefit from both intra and inter prediction. Objective evaluation shows significant improvement in coding efficiency over reference technologies.