Abstract
Many real-world sequences cannot be conveniently categorized as general or degenerate; in such cases, imposing a false dichotomy in using the fundamental matrix or
homography model for motion segmentation would lead
to difficulty. Even when we are confronted with a general scene-motion, the fundamental matrix approach as a
model for motion segmentation still suffers from several defects, which we discuss in this paper. The full potential of
the fundamental matrix approach could only be realized if
we judiciously harness information from the simpler homography model. From these considerations, we propose
a multi-view spectral clustering framework that synergistically combines multiple models together. We show that
the performance can be substantially improved in this way.
We perform extensive testing on existing motion segmentation datasets, achieving state-of-the-art performance on all
of them; we also put forth a more realistic and challenging dataset adapted from the KITTI benchmark, containing
real-world effects such as strong perspectives and strong
forward translations not seen in the traditional datasets.