Learning-based point cloud compression via unfolding of 3d point clouds

Abstract

In one implementation, we propose the UnfoldingOperator, which unfolds/flattens an unorganized input 3D point cloud onto a regular 2D grid. Given an input point cloud, an input 2D grid and the reconstructed point cloud produced by the FoldingNet, our proposal maps the input point cloud onto the 2D grid based on the reconstructed point cloud, leading to a 3-channel image. Alternatively, instead of using an image alone to represent a point cloud, the point cloud is decomposed into a codeword and a 3-channel residual image. This residual image is obtained by subtracting the reconstructed point cloud from the original input. The proposed UnfoldingOperator can be applied to point cloud compression, leading to a corresponding compression system that we call UnfoldingCompression. The UnfoldingCompression can work with the TearingCompression, where we can adaptively choose whether to use the UnfoldingCompression or TearingCompression.