Geometry-Guided 3D Data Interpolation for Projection-Based Dynamic Point Cloud Coding

With the recent improvements in acquisition techniques for 3D media applications, it has become easier to collect 3D data, for example, dynamic point cloud data. Such point clouds consist of a large amount of 3D coordinates, which describe a scene or object in 3D space by its geometry and texture attributes. Moreover, they are an effective representation of 3D environments for applications such as Augmented Reality or Virtual Reality. One of the main problems for such data is that the number of points is typically too large to allow for real-time transmission or efficient storage. Thus, compressing such 3D data is a key issue to reduce the amount of required bandwidth or memory. This paper presents a method for efficient compression of dynamic point cloud data within the current MPEG standardization framework for dynamic point cloud compression. The key benefit of the presented work is the reduced number of encoded and decoded 3D points compared to the reference framework, thus encoding and decoding complexity is reduced significantly. Objective results show a speed-up of around 35–40% in coding times. Furthermore, reconstruction quality is preserved, thus reducing bit rate requirements by up to 30%. Visual results verify the improved reconstruction quality, and compared to the reference at the same computational complexity, coding efficiency is improved by over 40%.