Consolidation of Multiple Depth Maps

Information

• Publication Type: Conference Paper
• Workgroup(s)/Project(s):
  • VRVis Competence Center
• Date: November 2011
• Location: Barcelona
• Lecturer: Irene Reisner-Kollmann
• Booktitle: IEEE Workshop on Consumer Depth Cameras for Computer Vision (CDC4CV 2011)

Abstract

Consolidation of point clouds, including denoising, outlier removal and normal estimation, is an important pre-processing step for surface reconstruction techniques. We present a consolidation framework specialized on point clouds created by multiple frames of a depth camera. An adaptive view-dependent locally optimal projection operator denoises multiple depth maps while keeping their struc ture in two-dimensional grids. Depth cameras produce a systematic variation of noise scales along the depth axis. Adapting to different noise scales allows to remove noise in the point cloud and preserve well-defined details at the same time. Our framework provides additional consolidation steps for depth maps like normal estimation and outlier removal. We show how knowledge about the distribution of noise in the input data can be effectively used for improving point clouds.

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BibTeX

@inproceedings{reisner-2011-comdm,
  title =      "Consolidation of Multiple Depth Maps",
  author =     "Irene Reisner-Kollmann and Stefan Maierhofer",
  year =       "2011",
  abstract =   "Consolidation of point clouds, including denoising, outlier
               removal and normal estimation, is an important
               pre-processing step for surface reconstruction techniques.
               We present a consolidation framework specialized on point
               clouds created by multiple frames of a depth camera. An
               adaptive view-dependent locally optimal projection operator
               denoises multiple depth maps while keeping their struc ture
               in two-dimensional grids. Depth cameras produce a systematic
               variation of noise scales along the depth axis. Adapting to
               different noise scales allows to remove noise in the point
               cloud and preserve well-defined details at the same time.
               Our framework provides additional consolidation steps for
               depth maps like normal estimation and outlier removal. We
               show how knowledge about the distribution of noise in the
               input data can be effectively used for improving point
               clouds.",
  month =      nov,
  location =   "Barcelona",
  booktitle =  "IEEE Workshop on Consumer Depth Cameras for Computer Vision
               (CDC4CV 2011)",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2011/reisner-2011-comdm/",
}