Generating Absolute-Scale Point Cloud Data of Built Infrastructure Scenes Using a Monocular Camera Setting

The global scale of Point Cloud Data (PCD) generated through monocular photo/videogrammetry is unknown, and can be calculated using at least one known dimension of the scene. Measuring one or more dimensions for this purpose induces a manual step in the 3D reconstruction process; this increases the effort and reduces the speed of reconstructing scenes, and induces substantial human error in the process due to the high level of measurement accuracy needed. Other ways of measuring such dimensions are based on acquiring additional information by either using extra sensors or specific classes of objects existing in the scene; we found that these solutions are not simple, cost effective or general enough to be considered practical for reconstructing both indoor and outdoor built infrastructure scenes. To address the issue, in this paper, we propose a novel method for automatically calculating the absolute scale of built infrastructure PCD. We use a pre-measured cube for outdoor scenes and a sheet of paper for indoor environments as the calibration patterns. Assuming that the dimensions of these objects are known, the proposed method extracts the objects’ corner points in 2D video frames using a novel algorithm. The extracted corner points are then matched between the consecutive frames. Finally, the corresponding corner points are reconstructed along with other features of the scenes to determine the real world scale. To evaluate the performance of the method, ten indoor and ten outdoor cases were selected and the absolute-scale PCD for each case was computed. Results illustrated the proposed algorithm is able to reconstruct the predefined objects with a high success rate while the generated absolute scale PCD is sufficiently accurate.