POINT CLOUD GEOMETRY SCALABLE CODING WITH A SINGLE END-TO-END DEEP LEARNING MODEL

Point clouds are gaining importance as the format to represent complex 3D objects and scenes, offering high user immersion and interaction, although at the cost of requiring massive data. Scalable coding is an important feature for point cloud coding, especially for real-time applications, where the fast and bitrate efficient access to a decoded point cloud is important; however, this issue is still rather unexplored in the literature. With the rise of deep learning methods as a promising solution for efficient coding, this paper proposes the first deep learning-based point cloud geometry scalable coding solution. Experimental results show that the proposed scalable coding solution consistently outperforms the MPEG standard for static point cloud geometry coding. In this way, a new research path is open for point cloud scalable coding technology.