Abstract
We address the problem of object segmentation from depth images of highly complex indoor scenes. We propose a model-free segmentation approach, which robustly separates unknown stacked objects in real-world scenes. Our approach constructs geometrically constrained 3D clusters known as salient-regions, which are subsequently merged into high-level object hypotheses by analyzing the local geometrical characteristics (such as local shape and homogeneity) of the area of their shared boundaries. We tested our approach using depth images from live Kinect video streams and publicly available RGB-D datasets. Our approach is highly efficient and achieves superior performance compared to state-of-the-art techniques.