Learning Convolutional Transforms for Lossy Point Cloud Geometry Compression

Abstract
Efficient point cloud compression is fundamental to enable the deployment of virtual and mixed reality applications, since the number of points to code can range in the order of millions. In this paper, we present a novel data-driven geometry compression method for static point clouds based on learned convolutional transforms and uniform quantization. We perform joint optimization of both rate and distortion using a trade-off parameter. In...
Paper Details
Title
Learning Convolutional Transforms for Lossy Point Cloud Geometry Compression
Published Date
Mar 20, 2019
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