TY - CPAPER AB - We propose a method which can perform real-time 3D reconstructionfrom a single hand-held event camera with no additional sensing,and works in unstructured scenes of which it has no prior knowledge.It is based on three decoupled probabilistic filters, each estimating 6-DoFcamera motion, scene logarithmic (log) intensity gradient and scene inversedepth relative to a keyframe, and we build a real-time graph ofthese to track and model over an extended local workspace. We alsoupgrade the gradient estimate for each keyframe into an intensity image,allowing us to recover a real-time video-like intensity sequence withspatial and temporal super-resolution from the low bit-rate input eventstream. To the best of our knowledge, this is the first algorithm provablyable to track a general 6D motion along with reconstruction of arbitrarystructure including its intensity and the reconstruction of grayscale videothat exclusively relies on event camera data. AU - Kim,H AU - Leutenegger,S AU - Davison,AJ DO - 10.1007/978-3-319-46466-4_21 EP - 364 PB - Springer PY - 2016/// SN - 0302-9743 SP - 349 TI - Real-time 3D reconstruction and 6-DoF tracking with an event camera UR - http://dx.doi.org/10.1007/978-3-319-46466-4_21 UR - http://hdl.handle.net/10044/1/38816 ER -