Multi-Depth-Map Raytracing for Efficient Large-Scene Reconstruction

Information

• Publication Type: Journal Paper (without talk)
• Workgroup(s)/Project(s):
  • Harvest4D - Harvesting Dynamic 3D Worlds from Commodity Sensor Clouds
• Date: February 2016
• DOI: 10.1109/TVCG.2015.2430333
• ISSN: 1077-2626
• Journal: IEEE Transactions on Visualization & Computer Graphics
• Number: 2
• Volume: 22
• Pages: 1127 – 1137

Abstract

With the enormous advances of the acquisition technology over the last years, fast processing and high-quality visualization of large point clouds have gained increasing attention. Commonly, a mesh surface is reconstructed from the point cloud and a high-resolution texture is generated over the mesh from the images taken at the site to represent surface materials. However, this global reconstruction and texturing approach becomes impractical with increasing data sizes. Recently, due to its potential for scalability and extensibility, a method for texturing a set of depth maps in a preprocessing and stitching them at runtime has been proposed to represent large scenes. However, the rendering performance of this method is strongly dependent on the number of depth maps and their resolution. Moreover, for the proposed scene representation, every single depth map has to be textured by the images, which in practice heavily increases processing costs. In this paper, we present a novel method to break these dependencies by introducing an efficient raytracing of multiple depth maps. In a preprocessing phase, we first generate high-resolution textured depth maps by rendering the input points from image cameras and then perform a graph-cut based optimization to assign a small subset of these points to the images. At runtime, we use the resulting point-to-image assignments (1) to identify for each view ray which depth map contains the closest ray-surface intersection and (2) to efficiently compute this intersection point. The resulting algorithm accelerates both the texturing and the rendering of the depth maps by an order of magnitude.

Additional Files and Images

Weblinks

• doi
• DOI: 10.1109/TVCG.2015.2430333

BibTeX

@article{arikan-2015-dmrt,
  title =      "Multi-Depth-Map Raytracing for Efficient Large-Scene
               Reconstruction",
  author =     "Murat Arikan and Reinhold Preiner and Michael Wimmer",
  year =       "2016",
  abstract =   "With the enormous advances of the acquisition technology
               over the last years, fast processing and high-quality
               visualization of large point clouds have gained increasing
               attention. Commonly, a mesh surface is reconstructed from
               the point cloud and a high-resolution texture is generated
               over the mesh from the images taken at the site to represent
               surface materials. However, this global reconstruction and
               texturing approach becomes impractical with increasing data
               sizes. Recently, due to its potential for scalability and
               extensibility, a method for texturing a set of depth maps in
               a preprocessing and stitching them at runtime has been
               proposed to represent large scenes. However, the rendering
               performance of this method is strongly dependent on the
               number of depth maps and their resolution. Moreover, for the
               proposed scene representation, every single depth map has to
               be textured by the images, which in practice heavily
               increases processing costs. In this paper, we present a
               novel method to break these dependencies by introducing an
               efficient raytracing of multiple depth maps. In a
               preprocessing phase, we first generate high-resolution
               textured depth maps by rendering the input points from image
               cameras and then perform a graph-cut based optimization to
               assign a small subset of these points to the images. At
               runtime, we use the resulting point-to-image assignments (1)
               to identify for each view ray which depth map contains the
               closest ray-surface intersection and (2) to efficiently
               compute this intersection point. The resulting algorithm
               accelerates both the texturing and the rendering of the
               depth maps by an order of magnitude.",
  month =      feb,
  doi =        "10.1109/TVCG.2015.2430333",
  issn =       "1077-2626",
  journal =    "IEEE Transactions on Visualization & Computer Graphics",
  number =     "2",
  volume =     "22",
  pages =      "1127--1137",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2016/arikan-2015-dmrt/",
}