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Time-consistent segmentation of indoor depth video frames

We propose a new method for segmenting indoor scenes captured with RGBD or depth cameras with a simple and fast iterative CPU segmentation. The segmentation consists of three main steps. In the first stage, regions grow by adding new pixels based on depth information and normal vectors. Next, multiple regions are merged into bigger surfaces based on adjacency and region statistics. The last stage of the method ensures interframe consistency based on a cost function between regions from subsequent frames. The planar segmentation can be used both for identifying indoor objects, which in general have planar faces, as well as for estimating the camera motion and then reconstructing the environment.