Automated image-based reconstruction of building interiors – a case study

Abstract

ABSTRACT

Depth camera indoor mapping for 3D virtual radio play

“…Recent studies (Furukawa et al., ; Georgantas et al., ; Xiao and Furukawa, ) show that automatic techniques, such as dense image matching, are suitable provided that the environment supports automatic feature matching, for example, using the scale‐invariant feature transform (SIFT; Lowe, ). However, these automated methods are sensitive to errors caused by reflective or textureless surfaces (Furukawa et al., ; Jancosek and Pajdla, ; Lehtola et al., ), even if the camera is pre‐calibrated in a laboratory (Fig. ).…”

Applying photogrammetry to reconstruct 3D luminance point clouds of indoor environments

“…Large, smooth, uniform and mono-coloured surfaces are problematic to capture and reconstruct accurately with photogrammetry (Lehtola, Kurkela, & Hyyppä, 2014). Such surfaces lack the unique features that are essential for photogrammetric reconstruction.…”

Radial Distortion from Epipolar Constraint for Rectilinear Cameras

“…Reference values for k 1 are in pixels per focal length units, while EPOS η values are in the chosen pixel per w/4 units. For result comparison, η results are transformed into k 1 units in Figure 5 and Table 1 by using a multiplication factor γ² = ( f /a) 2 , where f is the focal length from reference bundle adjustment, and a = w/4 is the chosen unit scale in EPOS. The checkerboard image (top) is corrected using the converged value of η obtained from checkerboard data; see Figure 5.…”

“…In contrast to capturing objects by encircling them with a camera, which is a well-studied problem (see, e.g., [1]), problems arise when capturing large-scale (indoor) environments with minimum image overlap in order to reduce the effort of image acquisition. Such a method, if "black-boxed", would enable multiple applications, for example, in real-estate management and brokering (see, e.g., [2]). …”