|dc.description.abstract||Videogrammetry is an affordable and easy-to-use technology for spatial 3D scene recovery. When applied to the civil engineering domain, a number of issues have to be taken into account.
First, videotaping large scale civil infrastructure scenes usually results in large video files filled with blurry, noisy, or simply redundant frames. This is often due to higher frame rate over camera speed ratio than necessary, camera and lens imperfections, and uncontrolled motions of the camera that results in motion blur. Only a small percentage of the collected video frames are required to achieve robust results. However, choosing the right frames is a tough challenge.
Second, the generated point cloud using a monocular videogrammetric pipeline is up to scale, i.e. the user has to know at least one dimension of an object in the scene to scale up the entire scene. This issue significantly narrows applications of generated point clouds in civil engineering domain since measurement is an essential part of every as-built documentation technology.
Finally, due to various reasons including the lack of sufficient coverage during videotaping of the scene or existence of texture-less areas which are common in most indoor/outdoor civil engineering scenes, quality of the generated point clouds are sometimes poor. This deficiency appears in the form of outliers or existence of holes or gaps on surfaces of point clouds. Several researchers have focused on this particular problem; however, the major issue with all of the currently existing algorithms is that they basically treat holes and gaps as part of a smooth surface. This approach is not robust enough at the intersections of different surfaces or corners while there are sharp edges. A robust algorithm for filling holes/gaps should be able to maintain sharp edges/corners since they usually contain useful information specifically for applications in the civil and infrastructure engineering domain.
To tackle these issues, this research presents and validates an improved videogrammetric pipeline for as built documentation of indoor/outdoor applications in civil engineering areas. The research consists of three main components:
1. Optimized selection of key frames for processing. It is necessary to choose a number of informative key frames to get the best results from the videogrammetric pipeline. This step is particularly important for outdoor environments as it is impossible to process a large number of frames existing in a large video clip.
2. Automated calculation of absolute scale of the scene. In this research, a novel approach for the process of obtaining absolute scale of points cloud by using 2D and 3D patterns is proposed and validated.
3. Point cloud data cleaning and filling holes on the surfaces of generated point clouds. The proposed algorithm to achieve this goal is able to fill holes/gaps on surfaces of point cloud data while maintaining sharp edges.
In order to narrow the scope of the research, the main focus will be on two specific applications:
1. As built documentation of bridges and building as outdoor case studies.
2. As built documentation of offices and rooms as indoor case studies.
Other potential applications of monocular videogrammetry in the civil engineering domain are out of scope of this research. Two important metrics, i.e. accuracy, completeness and processing time, are utilized for evaluation of the proposed algorithms.||