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Video Registration to SfM Models

T. Kroeger, L. Van Gool
European Conference on Computer Vision (ECCV)


Registering image data to Structure from Motion (SfM) point clouds is widely used to find precise camera location and orien- tation with respect to a world model. In case of videos one constraint has previously been unexploited: temporal smoothness. Without tem- poral smoothness the magnitude of the pose error in each frame of a video will often dominate the magnitude of frame-to-frame pose change. This hinders application of methods requiring stable poses estimates (e.g. tracking, augmented reality). We incorporate temporal constraints into the image-based registration setting and solve the problem by pose reg- ularization with model fitting and smoothing methods. This leads to accurate, gap-free and smooth poses for all frames. We evaluate differ- ent methods on challenging synthetic and real street-view SfM data for varying scenarios of motion speed, outlier contamination, pose estima- tion failures and 2D-3D correspondence noise. For all test cases a 2 to 60-fold reduction in root mean squared (RMS) positional error is ob- served, depending on pose estimation difficulty. For varying scenarios, different methods perform best. We give guidance which methods should be preferred depending on circumstances and requirements.

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  author = {T. Kroeger and L. Van Gool},
  title = {Video Registration to SfM Models},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year = {2014},
  keywords = {}