Scientific paper about reconstructing 3D models of buildings

First, most of it is unorganized, uncalibrated, have uncontrolled illumination, image quality and resolution and is widely variable. In essence, coming up with a computer vision technique that can work with most of these images has proved to be a challenge for most researchers. Now how can researchers work with this huge resource. this paper proposes solution such as Image Based Rendering algorithm and Structure from Motion. While a few other researchers such as Brown and Lowe (Lowe 395) have used Structure from Motion to tackle the above problems, the technique used in this paper has several modifications. Structure from Motion is effective in 3D visualization and scene modeling and can operate on hundreds of images obtained from keyword queries (photo tourism). Through photo tourism, it is possible to reconstruct many world sites. In effect, an algorithm that can work effectively on internet photos can enable vital applications such as 3D visualization, communication/media sharing, and localization. Two recent breakthroughs in the field of computer vision namely Structure from Motion and Feature Matching will be the backbone of this paper. Through these techniques, it is possible to reconstruct buildings in 3D to offer virtual and interactive tours for internet users. You can also evaluate the current state of a building and identify degradation and areas that may require renovation or reconstruction. Further, we can come up with creations or display of any building of interest as long as we have its image. Sparse geometry and camera reconstruction The browsing and visualization components of this system requires exact information in regards to the orientation, relative location and inherent parameters like focal lengths for each photo in a collection and sparse three dimension scene geometry. The system also requires a geo-referenced coordinate frame. For the most part, this information can be obtained through electronic components and Global Positioning System gadgets over the internet. Image files in EXIF tags often have this data though the vast majority of these sources are mostly inaccurate. As such, this system will compute this data via computer vision techniques. First, we will detect feature points in every image after which the system will equate feature points between pairs of images. Finally, the system will run an iterative Structure from Motion procedure to retrieve the camera parameters. Since Structure from Motion procedure will only produce estimates and our system requires absolute values, the system will run iterative procedure to acquire better estimates. How this whole procedure unfolds is detailed below. Detecting feature points will be done using SIFT keypoint detector (Lowe 411). This technique has better invariance to image alteration. The next step is matching keypoint descriptors using the approximate bordering neighbors. For instance, if we want to match two images I and J, first we will create a kd-tree obtained from element descriptors in J. Next, for each element in I we will locate an adjacent neighbor in J using the kd-tree. For effectiveness, we can use ANN’s priority search algorithm. This technique limits each query to visit a maximum of two hundred bins in the kd-tree. Alternatively, we can use a technique described by Lowe (Lowe 95). In the technique, for each