Abstract
Time-of-flight (TOF) depth cameras provide robust depth
inference at low power requirements in a wide variety of
consumer and industrial applications. These cameras reconstruct a single depth frame from a given set of infrared (IR)
frames captured over a very short exposure period. Operating in this mode the camera essentially forgets all information previously captured - and performs depth inference from
scratch for every frame. We challenge this practice and propose using previously captured information when inferring
depth. An inherent problem we have to address is camera
motion over this longer period of collecting observations.
We derive a probabilistic framework combining a simple but
robust model of camera and object motion, together with an
observation model. This combination allows us to integrate
information over multiple frames while remaining robust to
rapid changes. Operating the camera in this manner has implications in terms of both computational efficiency and how
information should be captured. We address these two issues
and demonstrate a realtime TOF system with robust temporal integration that improves depth accuracy over strong
baseline methods including adaptive spatio-temporal filters.