## 1 Introduction
### Problem Statement
In fact, increased changes in the image viewpoint can make the matching process unreliable, since the similarity between objects shown on images reduces [5]. For example, an image matching algorithm such as SIFT only works well when the difference between view angles is less than 30 degrees [41]. In addition, if the scaling is too high, the algorithm cannot detect the key points on the frame and the image matching process does not work correctly. For example, three images from a scene are illustrated in Figure 2. The first image (Figure 2a) is the reference image captured. The second image (Figure 2b) is the current frame from the same scene but impacted by the rotation of the camera (more than 30 degrees). The third image (Figure 2c) is also from the same scene but is impacted by high scaling. These scenarios can impact cases such as those using markerless AR that use SIFT and RANSAC during the image matching process. Thus, the algorithm cannot correctly match the features between two images. In addition, when image transformations take place,unrelated and unwanted areas around the scene are also detected. In this situation, change detection algorithms [42] report these areas as a change in the scene. This result is not accurate in construction scenarios where change detection algorithms are used to detect the construction progress based on changes in the image frames. In addition to the difficulties posed by image transformation, there is another scenario whereby image matching techniques could fail. For example, when a scene is completely changed during a renovation project, the image matching algorithms cannot match the distinct feature points between two frames (e.g., before and after renovation), therefore, the image matching cannot occur. Figure 3 shows a scene that is completely changed before and after renovation. This scenario can impact markerless AR. Figure 1: While SIFT detects, describes, and matches the key points, RANSAC filters incorrect matches (Adapted from [1,33]). Figure 3: An example of a scene in which its features have completely changed during renovation. (**a**) Image captured before renovation (reference image); (**b**) Image captured after renovation (current image). Figure 2: Differences between the reference and current images when the camera’s orientation and position change, which results in transformation of the features of the current images. (**a**) Reference frame; (**b**) Current frame impacted by rotation; (**c**) Current frame impacted by scaling. Another scenario in which image matching techniques fail to work accurately is when features in two scenes within a location (e.g., a room) are exchanged during renovation. For instance, before renovation, the reference images were captured from the west wall and north wall. During renovation, the features of the west wall and north wall were switched. Since a feature-based system can only detect environmental features and cannot interpret geographical directions, an image matching technique such as SIFT would fail to generate accurate results during the image matching process. To have a clear understanding of this scenario, two scenes have been sketched, as shown in Figure 4. Figure 4a shows a scene before renovation, a door is attached to the west wall, and a window is attached to the north wall. Figure 4b shows the same room, but this time the window has been moved to the west wall and the door has been moved to the north wall. In fact, feature-based tracking methods detect environmental features but not directions. This scenario can impact use cases like markerless AR and change detection. These limitations of the image matching process motivated us to study supplementary ways (e.g., controlling the image capturing process) to support the image matching algorithms in order to prevent sole dependency on natural features in the scene.