Real-Time 3D Reconstruction and 6-DoF Tracking with an Event Camera

author: Hanme Kim, Department of Computing, Imperial College London
published: Oct. 24, 2016,   recorded: October 2016,   views: 69
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Description

We propose a method which can perform real-time 3D reconstruction from 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-DoF camera motion, scene logarithmic (log) intensity gradient and scene inverse depth relative to a keyframe, and we build a real-time graph of these to track and model over an extended local workspace. We also upgrade the gradient estimate for each keyframe into an intensity image, allowing us to recover a real-time video-like intensity sequence with spatial and temporal super-resolution from the low bit-rate input event stream. To the best of our knowledge, this is the first algorithm provably able to track a general 6D motion along with reconstruction of arbitrary structure including its intensity and the reconstruction of grayscale video that exclusively relies on event camera data.

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Download slides icon Download slides: eccv2016_kim_event_camera_01.pdf (11.8┬áMB)


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