3D Point Cloud Compression with Recurrent Neural Network and Image Compression Methods

Storing and transmitting LiDAR point cloud data is essential for many AV applications, such as training data collection, remote control, cloud services or SLAM. However, due to the sparsity and unordered structure of the data, it is difficult to compress point cloud data to a low volume. Transformin...

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Bibliographic Details
Published in:arXiv.org 2024-02
Main Authors: Beemelmanns, Till, Tao, Yuchen, Lampe, Bastian, Reiher, Lennart, Raphael van Kempen, Woopen, Timo, Eckstein, Lutz
Format: Article
Language:English
Subjects:
Algorithms
Data collection
Data compression
Image compression
Lidar
Neural networks
Octrees
Performance evaluation
Recurrent neural networks
Remote control
Representations
Source code
Three dimensional models
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Summary:Storing and transmitting LiDAR point cloud data is essential for many AV applications, such as training data collection, remote control, cloud services or SLAM. However, due to the sparsity and unordered structure of the data, it is difficult to compress point cloud data to a low volume. Transforming the raw point cloud data into a dense 2D matrix structure is a promising way for applying compression algorithms. We propose a new lossless and calibrated 3D-to-2D transformation which allows compression algorithms to efficiently exploit spatial correlations within the 2D representation. To compress the structured representation, we use common image compression methods and also a self-supervised deep compression approach using a recurrent neural network. We also rearrange the LiDAR's intensity measurements to a dense 2D representation and propose a new metric to evaluate the compression performance of the intensity. Compared to approaches that are based on generic octree point cloud compression or based on raw point cloud data compression, our approach achieves the best quantitative and visual performance. Source code and dataset are available at https://github.com/ika-rwth-aachen/Point-Cloud-Compression.
ISSN:2331-8422
DOI:10.48550/arxiv.2402.11680