Abstract
Abstract. The ongoing development of advanced techniques in photogrammetry, computer vision (CV), robotics and laser scanning to efficiently acquire three dimensional geometric data offer new possibilities for many applications. The output of these techniques in the digital form is often a sparse or dense point cloud describing the 3D shape of an object. Viewing these point clouds in a computerized digital environment holds a difficulty in displaying the visible points of the object from a given viewpoint rather than the hidden points. This visibility problem is a major computer graphics topic and has been solved previously by using different mathematical techniques. However, to our knowledge, there is no study of presenting the different visibility analysis methods of point clouds from a photogrammetric viewpoint. The visibility approaches, which are surface based or voxel based, and the hidden point removal (HPR) will be presented. Three different problems in close range photogrammetry are presented: camera network design, guidance with synthetic images and the gap detection in a point cloud. The latter one introduces also a new concept of gap classification. Every problem utilizes a different visibility technique to show the valuable effect of visibility analysis on the final solution.
Highlights
Computing the visible part of a 3D object is a vital problem in computer graphics, computer vision, robotics, GIS and photogrammetry
Three strategies are described to analyze the visibility of 3D point clouds and these are
The use of visibility analysis is quite important in many photogrammetric applications and especially when the data set type is a point cloud
Summary
Computing the visible part of a 3D object is a vital problem in computer graphics, computer vision, robotics, GIS and photogrammetry. The method of hidden point removal HPR (Katz et al, 2007) is widely applied for the visibility analysis The advantage of this technique is to avoid creating a surface from the point cloud which might be expensive and this led to analyze visibility efficiently with both sparse and dense clouds. After we create the surface, the notion of visibility can be uniquely defined and find its hidden and visible points from any viewpoint This is mathematically achieved by either intersecting the line of sight rays with the surface triangles or checking the orientation of the surface normal. We will demonstrate the necessity of using the techniques of visibility analysis in solving three different photogrammetric problems These visibility methods are: The surface triangle based methods (the normal direction testing, triangle – ray intersection, Z – buffering method). While the two former examples are extracted from our previous work, the latter one introduces a new concept of gap classification
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