  Detecting and segmenting moving objects from a moving monocular camera is
challenging in the presence of unknown camera motion, diverse object motions
and complex scene structures. Most existing methods rely on a single motion cue
to perform motion segmentation, which is usually insufficient when facing
different complex environments. While a few recent deep learning based methods
are able to combine multiple motion cues to achieve improved accuracy, they
depend heavily on vast datasets and extensive annotations, making them less
adaptable to new scenarios. To address these limitations, we propose a novel
monocular dense segmentation method that achieves state-of-the-art motion
segmentation results in a zero-shot manner. The proposed method synergestically
combines the strengths of deep learning and geometric model fusion methods by
performing geometric model fusion on object proposals. Experiments show that
our method achieves competitive results on several motion segmentation datasets
and even surpasses some state-of-the-art supervised methods on certain
benchmarks, while not being trained on any data. We also present an ablation
study to show the effectiveness of combining different geometric models
together for motion segmentation, highlighting the value of our geometric model
fusion strategy.
