Linear and bilinear subspace methods for structure from motion
Mei Han, Takeo Kanade · 2001
Structure from Motion (SFM), which is recovering camera motion and scene structure from image sequences, has various applications, such as scene modeling, robot navigation and object recognition. Most of previous research on SFM requires simplifying assumptions on the camera or the scene. Common assumptions are a) the camera intrinsic parameters, such as focal lengths, are known or unchanged throughout the sequence, and/or b) the scene does not contain moving objects. In practice, these are unrealistic assumptions. In this thesis we present a collection of reconstruction methods for dealing with image sequences taken with uncalibrated cameras and/or of multiple motion scenes. The methods produce Euclidean reconstruction directly from feature point locations and are based on the bilinear relationship of camera motion and scene structure. For uncalibrated image sequences, we embed the camera intrinsic parameters within the camera motion representation. For image sequences of multiple motion scenes, we incorporate multiple motions into the scene structure representation. In this way, we derive linear and bilinear subspace constraints on the large amount of information integrated over the entire image sequences. By taking advantage of this redundant information we can achieve accurate and reliable reconstruction.