Paper Info
Title
Real-Time 3d Reconstruction And 6-Dof Tracking With An Event Camera
Abstract
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.
Year
DOI
Venue
2016
10.1007/978-3-319-46466-4_21
COMPUTER VISION - ECCV 2016, PT VI
Keywords
Field
DocType
6-DoF tracking, 3D reconstruction, Intensity reconstruction, Visual odometry, SLAM, Event-based camera
Computer vision,Inverse,Visual odometry,Workspace,Computer science,Upgrade,Artificial intelligence,Logarithm,Probabilistic logic,Grayscale,3D reconstruction
Conference
Volume
ISSN
Citations 
9910
0302-9743
47
PageRank 
References 
Authors
1.43
16
3
Name
Order
Citations
PageRank
Hanme Kim1813.63
Stefan Leutenegger2137961.81
Andrew J. Davison36707350.85