<ul><li>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.</li><li>Most existing methods rely on a single motion cue to perform motion segmentation, which is usually insufficient when facing different complex environments.</li><li>A novel monocular dense segmentation method is proposed, combining the strengths of deep learning and geometric model fusion methods.</li><li>The method achieves state-of-the-art motion segmentation results in a zero-shot manner, surpassing some supervised methods without training on any data.</li></ul>

Best Research on Geometric Deep Learning part1

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