Abstract
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.
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