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RGBD images with high quality annotations in the form of geometric (i.e.,segmentation) and structural (i.e., how do the segments are mutually related in3D) information provide valuable priors to a large number of scene and imagemanipulation applications. While it is now simple to acquire RGBD images,annotating them, automatically or manually, remains challenging especially incluttered noisy environments. We present SmartAnnotator, an interactive systemto facilitate annotating RGBD images. The system performs the tedious tasks ofgrouping pixels, creating potential abstracted cuboids, inferring objectinteractions in 3D, and comes up with various hypotheses. The user simply hasto flip through a list of suggestions for segment labels, finalize a selection,and the system updates the remaining hypotheses. As objects are finalized, theprocess speeds up with fewer ambiguities to resolve. Further, as more scenesare annotated, the system makes better suggestions based on structural andgeometric priors learns from the previous annotation sessions. We test oursystem on a large number of database scenes and report significant improvementsover naive low-level annotation tools.

SmartAnnotator: An Interactive Tool for Annotating RGBD Indoor Images