Motion segmentation of truncated signed distance function based volumetric surfaces

Truncated signed distance function (TSDF) based volumetric surface reconstructions of static environments can be readily acquired using recent RGB-D camera based mapping systems. If objects in the environment move then a previously obtained TSDF reconstruction is no longer current. Handling this problem requires segmenting moving objects from the reconstruction. To this end, we present a novel solution to the motion segmentation of TSDF volumes. The segmentation problem is cast as CRF-based MAP inference in the voxel space. We propose: a novel data term by solving sparse multi-body motion segmentation and computing likelihoods for each motion label in the RGB-D image space, and, a novel pair wise term based on gradients of the TSDF volume. Experimental evaluation shows that the proposed approach achieves successful segmentations on reconstructions acquired with Kinect Fusion. Unlike the existing solutions which only work if the objects move completely from their initially occupied spaces, the proposed method permits segmentation of objects when they start to move.