Depth sensors have become increasingly popular in in- teractive computer vision applications. Currently, most of these applications are limited to indoor use. Popular IR- based depth sensors cannot provide depth data when ex- posed to sunlight. In these cases, one can still obtain depth information using a stereo camera set up or a special out- door Time-of-Flight camera, at the cost of a reduced qual- ity of the depth image. The resulting depth images are often incomplete and suffer from low resolution, noise and miss- ing information. The aim of this paper is to recover the missing depth information based on an extension of SEEDS superpixels . The superpixel segmentation algorithm is extended to take depth information into account where available. The approach takes advantage of the boundary- updating property of SEEDS. The result is a clean segmen- tation that recovers the missing depth information in a low- quality depth image. We test the approach outdoors on an interactive urban robot. The system is used to segment a person in front of the robot, and to detect body parts for interaction with the robot using pointing gestures.