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Recently, 3D visual representation models such as light fields and point clouds are becoming popular due to their capability to represent the real world in a more complete and immersive way, paving the road for new and more advanced visual experiences. The point cloud representation model is able to efficiently represent the surface of objects/scenes by means of a set of 3D points and associated attributes and is increasingly being used from autonomous cars to augmented reality. Emerging imaging sensors have made it easier to perform richer and denser point cloud acquisitions, notably with millions of points, making it impossible to store and transmit these very high amounts of data without appropriate coding. This bottleneck has raised the need for efficient point cloud coding solutions in order to offer more immersive visual experiences and better quality of experience to the users. In this context, this paper proposes an efficient lossy coding solution for the geometry of static point clouds. The proposed coding solution uses an octree-based approach for a base layer and a graph-based transform approach for the enhancement layer where an Inter-layer residual is coded. The performance assessment shows very significant compression gains regarding the state-of-the-art, especially for the most relevant lower and medium rates.
Rente et al. (Wed,) studied this question.