3D Scan Integration

Information

• Publication Type: Bachelor Thesis
• Workgroup(s)/Project(s):
  • Modeling the World at Scale
• Date: March 2021
• Date (Start): 16. March 2020
• Date (End): 1. March 2021
• Matrikelnummer: 01632716
• First Supervisor: Stefan Ohrhallinger
• Keywords: 3d scanning, scan integration, geometry processing

Abstract

3D scanning is often not complete after a single pass from a single sensor. Multiple scanners, e.g. from a crowd, or multiple autonomous vehicles, may contribute data simultaneously. Or, after looking at the resulting model, more passes may be made to fill holes or improve the quality.

This requires updating a 3D reconstruction with new points, integrating those into the model and considering them equally with the existing points. In order to avoid dynamic and massive storage requirements, their coordinate information required for reconstruction can be stored as single median+variance vectors, which can be updated incrementally with new points, see e.g.: http://datagenetics.com/blog/november22017/index.html). With the local information at nodes, marching cubes can be used to generate a triangulation at grid cells. Since the octree has varying depths at leaf nodes, we need to apply an adapted version from an existing algorithm, Screened Poisson (for source and paper see: http://www.cs.jhu.edu/~misha/Code/PoissonRecon/Version8.0/). See also http://infinitam.org for the source code and the paper it is based on.

Tasks: Use a Kinect 3D scanner with the Infinitam software to scan several overlapping passes of an interior room, resulting in an octree with data in its nodes Hand-align scans with Meshlab or register them using ICP (http://pointclouds.org/documentation/tutorials/iterative_closest_point.php) so that they correspond spatially Add new points into octree nodes which overlap in space Apply a provided surface orientation operator which uses median+variance of nodes in order to mark vertices of nodes as in- or outside Create a mesh from the octree on demand for visualization, using marching cubes adapted to octrees as in Screened Poisson Evaluate the quality of the incrementally created reconstruction with a single-pass reconstruction of a merged point cloud where all points are considered at once

Additional Files and Images

Weblinks

• source

BibTeX

@bachelorsthesis{depner_dennis-2020-baa,
  title =      "3D Scan Integration",
  author =     "Dennis Depner",
  year =       "2021",
  abstract =   "3D scanning is often not complete after a single pass from a
               single sensor. Multiple scanners, e.g. from a crowd, or
               multiple autonomous vehicles, may contribute data
               simultaneously. Or, after looking at the resulting model,
               more passes may be made to fill holes or improve the
               quality.  This requires updating a 3D reconstruction with
               new points, integrating those into the model and considering
               them equally with the existing points. In order to avoid
               dynamic and massive storage requirements, their coordinate
               information required for reconstruction can be stored as
               single median+variance vectors, which can be updated
               incrementally with new points, see e.g.:
               http://datagenetics.com/blog/november22017/index.html). With
               the local information at nodes, marching cubes can be used
               to generate a triangulation at grid cells. Since the octree
               has varying depths at leaf nodes, we need to apply an
               adapted version from an existing algorithm, Screened Poisson
                (for source and paper see:
               http://www.cs.jhu.edu/~misha/Code/PoissonRecon/Version8.0/).
               See also http://infinitam.org for the source code and the
               paper it is based on.  Tasks:     Use a Kinect 3D scanner
               with the Infinitam software to scan several overlapping
               passes of an interior room, resulting in an octree with data
               in its nodes     Hand-align scans with Meshlab or register
               them using ICP
               (http://pointclouds.org/documentation/tutorials/iterative_closest_point.php)
               so that they correspond spatially     Add new points into
               octree nodes which overlap in space     Apply a provided
               surface orientation operator which uses median+variance of
               nodes in order to mark vertices of nodes as in- or outside  
                 Create a mesh from the octree on demand for visualization,
               using marching cubes adapted to octrees as in Screened
               Poisson     Evaluate the quality of the incrementally
               created reconstruction with a single-pass reconstruction of
               a merged point cloud where all points are considered at once
               ",
  month =      mar,
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  school =     "Research Unit of Computer Graphics, Institute of Visual
               Computing and Human-Centered Technology, Faculty of
               Informatics, TU Wien ",
  keywords =   "3d scanning, scan integration, geometry processing",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2021/depner_dennis-2020-baa/",
}