Volume XLII-2/W5
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W5, 343-347, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W5-343-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W5, 343-347, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W5-343-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  18 Aug 2017

18 Aug 2017

INTERACTIVE CLASSIFICATION OF CONSTRUCTION MATERIALS: FEEDBACK DRIVEN FRAMEWORK FOR ANNOTATION AND ANALYSIS OF 3D POINT CLOUDS

M. R. Hess1, V. Petrovic2, and F. Kuester1 M. R. Hess et al.
  • 1Dept. of Structural Engineering, University of California, San Diego, USA
  • 2Dept. of Computer Science and Engineering, University of California, San Diego, USA

Keywords: Point Cloud, Visualization, Interactive, Annotation, Classification

Abstract. Digital documentation of cultural heritage structures is increasingly more common through the application of different imaging techniques. Many works have focused on the application of laser scanning and photogrammetry techniques for the acquisition of threedimensional (3D) geometry detailing cultural heritage sites and structures. With an abundance of these 3D data assets, there must be a digital environment where these data can be visualized and analyzed. Presented here is a feedback driven visualization framework that seamlessly enables interactive exploration and manipulation of massive point cloud data. The focus of this work is on the classification of different building materials with the goal of building more accurate as-built information models of historical structures. User defined functions have been tested within the interactive point cloud visualization framework to evaluate automated and semi-automated classification of 3D point data. These functions include decisions based on observed color, laser intensity, normal vector or local surface geometry. Multiple case studies are presented here to demonstrate the flexibility and utility of the presented point cloud visualization framework to achieve classification objectives.