Thibaut Meyer, Dr.-Ing.
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Researcher
Video Analysis and Content Recognition Group
Personal website
Research Topic
Point cloud sequence coding using graph signal processing
Dynamic 3D scenes are the center of a growing number of emerging applications such as immersive communication, gaming or animation.
They are often represented as point cloud sequences, which are becoming increasingly easy to obtain, as the hardware that is necessary to capture them is more and more ubiquitous.
Thus emerges the need to compress this very specific type of data.
In the meantime, Graph Signal Processing is a growing scientific field, proving particularly useful for representing and processing irregular and sparse data. Indeed, point clouds are very sparse, and considering the entire 3D space where they live when processing them would be a wasteful approach.
The focus of my research is the inclusion of graph signal processing in several steps of a point cloud sequence compression pipeline, and the study of its benefits. As a first step, after having calculated an equivalent graph representation of a point cloud, I’m looking closely at the utilization of spectral features computed in the graph spectral domain in order to find correspondences between two subsequent point clouds.
Second, I’m investigating the feasibility of coding the attributes of a point cloud in the equivalent graph frequency domain, which is hopefully a more compact and efficient way of representing the attributes in order to store or transmit a point cloud sequence.
Publications
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