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Depth-from-trajectories for uncalibrated multiview video

We propose a method for efficiently determining qualitative depth maps for multiple monoscopic videos of the same scene without explicitly solving for stereo or calibrating any of the cameras involved. By tracking a small number of feature points and determining trajectory correspondence, it is possible to determine correct temporal alignment as well as establish a similarity metric for fundamental matrices relating each trajectory. Modeling of matrix relations with a weighted digraph and performing Markov clustering results in a determination of emergent depth layers for feature points. Finally, pixels are segmented into depth layers based upon motion similarity to feature point trajectories. Initial experimental results are demonstrated on stereo benchmark and consumer data.