Efficient Dense 3D Rigid-Body Motion Segmentation in RGB-D Video
published: April 3, 2014, recorded: September 2013, views: 2498
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Motion is a fundamental segmentation cue in video. Many current approaches segment 3D motion in monocular or stereo image sequences, mostly relying on sparse interest points or being dense but computationally demanding. We propose an efficient expectation-maximization (EM) framework for dense 3D segmentation of moving rigid parts in RGB-D video. Our approach segments two images into pixel regions that undergo coherent 3D rigid-body motion. Our formulation treats background and foreground objects equally and poses no further assumptions on the motion of the camera or the objects than rigidness. While our EM-formulation is not restricted to a specific image representation, we supplement it with efficient image representation and registration for rapid segmentation of RGB-D video. In experiments we demonstrate that our approach recovers segmentation and 3D motion at good precision.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !