Real-time Point Cloud Compression

In proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015
 

Abstract

With today's advanced 3D scanner technology, huge amounts of point cloud data can be generated in short amounts of time. Data compression is thus necessary for storage and especially for transmission, e.g., via wireless networks. While previous approaches delivered good compression ratios and interesting theoretical insights, they are either computationally expensive or do not support incrementally acquired data and locally decompressing the data, two requirements we found necessary in many applications. We present a compression approach that is efficient in storage requirements as well as in computational cost, as it can compress and decompress point cloud data in real-time. Furthermore, it is capable of compressing incrementally acquired data, local decompression and of decompressing a subsampled representation of the original data. Our method is based on local 2D parameterizations of surface point cloud data, for which we describe an efficient approach. We suggest the usage of standard image compression techniques for the compression of local details. While exhibiting state-of-the-art compression ratios, our approach remains easy to implement. In our evaluation, we compare our approach to previous ones and discuss the choice of parameters. Due to our algorithm's efficiency, we consider it as a reference concerning speed and compression rates.

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Bibtex

@INPROCEEDINGS{Golla2015RealtimePointCloudCompression,
     author = {Golla, Tim and Klein, Reinhard},
      title = {Real-time Point Cloud Compression},
    journal = {Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
       year = {2015},
       note = {to appear, Conference 28 September - 3 October 2015},
   abstract = {With today's advanced 3D scanner technology, huge amounts of point cloud data can be generated in
               short amounts of time. Data compression is thus necessary for storage and especially for
               transmission, e.g., via wireless networks. While previous approaches delivered good compression
               ratios and interesting theoretical insights, they are either computationally expensive or do not
               support incrementally acquired data and locally decompressing the data, two requirements we found
               necessary in many applications. We present a compression approach that is efficient in storage
               requirements as well as in computational cost, as it can compress and decompress point cloud data in
               real-time. Furthermore, it is capable of compressing incrementally acquired data, local
               decompression and of decompressing a subsampled representation of the original data. Our method is
               based on local 2D parameterizations of surface point cloud data, for which we describe an efficient
               approach. We suggest the usage of standard image compression techniques for the compression of local
               details. While exhibiting state-of-the-art compression ratios, our approach remains easy to
               implement. In our evaluation, we compare our approach to previous ones and discuss the choice of
               parameters. Due to our algorithm's efficiency, we consider it as a reference concerning speed and
               compression rates.}
}