Here is one highly cited paper comparing various techniques:Ī Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms.
Manual selection: have a person hand-select matching points.There is currently still no ideal technique to solve the correspondence problem, but possible approaches could fall in these categories: This helps you to use faster local techniques. If you already have the fundamental matrix, it will allow you to rectify the images such that corresponding points in two images will be constrained to a line (in theory). Techniques for this either work locally by looking for a best match in a small region around each point, or globally by considering the image as a whole. Correspondence problemįinding corresponding points is the tricky part that requires you to look for points of the same brightness or colour, or to use texture patterns or some other features to identify the same points in pairs of images. If you want a method that doesn't need the camera parameters and works for unknown camera set-ups you should probably look into methods for uncalibrated stereo reconstruction. This requires some theory about co-ordinate projections with homogeneous co-ordinates and also knowledge of the pinhole camera model and camera matrix. You can calculate the fundamental and essential matrices using only matrix theory and use these to rectify your images. 8 or more physical points with matching known positions in two photos (when using the eight-point algorithm).the camera's position and rotation (it's extrinsic parameters), and.the intrinsic camera parameters (requiring camera calibration),.This simplifies finding corresponding points as well as the final triangulation calculations. Stereo reconstruction is usually done by first calibrating your camera setup so you can rectify your images using the theory of epipolar geometry. You need to find corresponding points such that you can then use triangulation to find the 3D co-ordinates of the points. Performing stereo reconstruction requires that pairs of images are taken that have a good amount of visible overlap of physical points. It is usually approached by solving the stereo-view reconstruction problem for each pair of consecutive images. Then parts will need to differ in color to help separate out the various parts of the model I expect also.Īs mentioned, the problem is very hard and is often also referred to as multi-view object reconstruction. Right now I am considering showing the house, then the user can put in some assistance for height, such as distance from the camera to the top of that part of the model, and given enough of this it would be possible to start calculating heights for the rest, especially if there is a top-down image, then pictures from angles on the four sides, to calculate relative heights. What language you explain in doesn't matter, as I am looking for the best approach.
If I sit down and think about taking more than one picture, labeling direction, and distance, I should be able to figure out how to do this, but, I thought I would ask if someone has some paper that may help explain more. If I take a picture with a camera, so I know the distance from the camera to the object, such as a scale model of a house, I would like to turn this into a 3D model that I can maneuver around so I can comment on different parts of the house.