Octree-based Point-Cloud Compression

M. Botsch und B. Chen (Editoren)
In proceedings of Symposium on Point-Based Graphics 2006, Eurographics, Juli 2006
Präsentiert: Symposium on Point-Based Graphics 2006
 

Abstract

In this paper we present a progressive compression method for point sampled models that is specifically apt at dealing with densely sampled surface geometry. The compression is lossless and therefore is also suitable for storing the unfiltered, raw scan data. Our method is based on an octree decomposition of space. The point-cloud is encoded in terms of occupied octree-cells. To compress the octree we employ novel prediction techniques that were specifically designed for point sampled geometry and are based on local surface approximations to achieve high compression rates that outperform previous progressive coders for point-sampled geometry. Moreover we demonstrate that additional point attributes, such as color, which are of great importance for point-sampled geometry, can be well integrated and efficiently encoded in this framework.

Stichwörter: point-based rendering, unprocessed point clouds

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Bibtex

@INPROCEEDINGS{schnabel-2006-octree,
      author = {Schnabel, Ruwen and Klein, Reinhard},
      editor = {Botsch, M. and Chen, B.},
       title = {Octree-based Point-Cloud Compression},
   booktitle = {Symposium on Point-Based Graphics 2006},
        year = {2006},
       month = jul,
   publisher = {Eurographics},
    keywords = {point-based rendering, unprocessed point clouds},
    abstract = {In this paper we present a progressive compression method for point 
                sampled models that is specifically apt at
                dealing with densely sampled surface geometry. The compression is 
                lossless and therefore is also suitable for storing
                the unfiltered, raw scan data. Our method is based on an octree 
                decomposition of space. The point-cloud is
                encoded in terms of occupied octree-cells. To compress the octree we 
                employ novel prediction techniques that were
                specifically designed for point sampled geometry and are based on local 
                surface approximations to achieve high
                compression rates that outperform previous progressive coders for 
                point-sampled geometry. Moreover we demonstrate
                that additional point attributes, such as color, which are of great 
                importance for point-sampled geometry,
                can be well integrated and efficiently encoded in this framework.},
  conference = {Symposium on Point-Based Graphics 2006}
}