Double-Deep Learning-Based Point Cloud Geometry Coding with Adaptive Super-Resolution

Point clouds represent 3D visual data in a very immersive and realistic way, offering to the users a large degree of navigation and interaction. For some key use cases, point clouds are potentially lighter and easier to acquire than other 3D representation models, thus offering an alternative with lower computational cost. To offer visual realistic and immersive experiences, notably the illusion of well-formed surfaces, point clouds typically require a large number of points. To make its storage and transmission feasible, efficient point cloud coding is essential. Recently, deep learning-based point cloud coding solutions have proven to be competitive in compression performance, excelling in distinct scenarios, although struggling to achieve similar results for sparser point clouds and lower coding rates. To tackle these limitations, this paper proposes a double-deep learning-based approach for point cloud coding by integrating a super-resolution tool. The main idea consists on converting sparser point clouds into denser ones via a down-sampling step performed before coding. Since this is a lossy process, a super-resolution step is included after decoding to mitigate the point losses and bringing the point cloud to the initial resolution. Furthermore, the sampling factor can be adaptively selected, thus offering additional flexibility to the point cloud characteristics. The proposed double-deep coding and super-resolution solution outperforms both the G-PCC Octree and V-PCC Intra point cloud coding standards achieving, respectively, 81.9% and 22.3% rate reduction measured as BD-Rate for the PSNR D1 metric.