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Guest Editorial Introduction to the Special Issue on Recent Advances in Point Cloud Processing and Compression

A POINT cloud is a set of 3D points that can be used to represent a 3D surface. Each point has a spatial position (x, y, z) and a vector of attributes, such as colors, material reflection, or normal. As point clouds are capable of reconstructing 3D objects or scenes, they have the potential to be widely used in various applications such as auto-driving and 6-degree virtual reality. However, the following properties of point cloud make the point cloud compression and processing become rather challenging. 1) Unstructured. The point cloud is a series of non-uniform sampled points. On the one hand, it makes the correlations among various points difficult to be utilized for compression. On the other hand, the convolutional neural network that is widely used in image/video processing cannot be applied to the point cloud processing. 2) Unordered. Unlike images and videos, the point cloud is a set of points without a specific order. Therefore, both the point cloud processing and compression algorithms need to be invariant to any permutations of the input point clouds. Recent years have witnessed considerable research efforts in point cloud processing and compression. Hence, the state-ofthe-art in point cloud processing and compression is getting redefined. Yet, many research challenges still remain to be addressed. 1) Efficient framework to compress the point cloud especially the sparse point cloud is still an open problem. Both the traditional compression framework from MPEG and the end-to-end point cloud compression schemes are competing to be the state-of-the-art. 2) Simple yet efficient quality metrics to balance the trade-off between the geometry and the attribute that can reflect human perception are needed. Point clouds have two kinds of information: geometry and attribute. They have their own quality metrics individually. However, how do the qualities of geometry and attributes influence the overall qualities of point clouds are still unknown. A good quality metric is also important to guide the processing and compression algorithms. 3) Efficient deep-learning network structures for both point cloud low-level and high-level vision tasks are anticipated. The development of point cloud object detection and segmentation is still at an initial stage. More sophisticated and efficient algorithms are to be developed.