Foreground segmentation with efficient selection from ICP outliers in 3D scene

Abstract

Foreground segmentation enables dynamic reconstruction of the moving objects in static scenes. After KinectFusion had proposed a novel method that constructs the foreground from the Iterative Closest Point (ICP) outliers, numerous studies proposed filtration methods to reduce outlier noise. To this end, the relationship between outliers and the foreground is investigated, and a method to efficiently extract the foreground from outliers is proposed. The foreground is found to be directly connected to ICP distance outliers rather than the angle and distance outliers that have been used in past research. Quantitative results show that the proposed method outperforms prevalent foreground extraction methods, and attains an average increase of 11.8% in foreground quality. Moreover, real-time speed of 50 fps is achieved without heavy graph-based refinements, such as GrabCut. The proposed depth features surpass current 3D GrabCut, which only uses RGB-N.