Incremental attention-driven object segmentation

Segmentation of highly cluttered indoor scenes is a challenging task and should be solved in real time to be efficiently used in such applications as robotics, for example. Traditional segmentation methods are often overwhelmed by the complexity of the scene and require significant processing time. To tackle this problem we propose to use incremental attention-driven segmentation, where attention mechanisms are used to prioritize parts of the scene to be handled first. Our method outputs object hypotheses composed of parametric surface models. We evaluate our approach on two publicly available datasets of cluttered indoor scenes. We show that the proposed method outperforms existing methods of attention-driven segmentation in terms of segmentation quality and computational performance.