Variable Rate Compression for Raw 3D Point Clouds

In this paper, we propose a novel variable rate deep compression architecture that operates on raw 3D point cloud data. The majority of learning-based point cloud compression methods work on a downsampled representation of the data. Moreover, many existing techniques require training multiple networ...

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Vydáno v:	2022 International Conference on Robotics and Automation (ICRA) s. 8748 - 8755
Hlavní autoři:	Al Muzaddid, Md Ahmed, Beksi, William J.
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 23.05.2022
Témata:	Automation Big Data in Robotics and Automation Bit rate Computational modeling Deep Learning for Visual Perception Point cloud compression RGB-D Perception Three-dimensional displays Training Visualization
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Popis
Shrnutí:	In this paper, we propose a novel variable rate deep compression architecture that operates on raw 3D point cloud data. The majority of learning-based point cloud compression methods work on a downsampled representation of the data. Moreover, many existing techniques require training multiple networks for different compression rates to generate consolidated point clouds of varying quality. In contrast, our network is capable of explicitly processing point clouds and generating a compressed description at a comprehensive range of bitrates. Furthermore, our approach ensures that there is no loss of information as a result of the voxelization process and the density of the point cloud does not affect the encoder/decoder performance. An extensive experimental evaluation shows that our model obtains state-of-the-art results, it is computationally efficient, and it can work directly with point cloud data thus avoiding an expensive voxelized representation.
DOI:	10.1109/ICRA46639.2022.9812239