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KinectFusion: real-time dense surface mapping and tracking

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

  • Richard A. Newcombe
  • Shahram Izadi
  • Otmar Hilliges
  • David Molyneaux
  • David Kim
  • Andrew J. Davison
  • Pushmeet Kohli
  • Jamie Shotton
  • Steve Hodges
  • Andrew Fitzgibbon
Publication date2011
Host publicationMixed and Augmented Reality (ISMAR), 2011 10th IEEE International Symposium on
Place of PublicationWashington, DC, USA
PublisherIEEE Computer Society
Number of pages10
ISBN (electronic)9781457721847
ISBN (print)9781457721830
<mark>Original language</mark>English


We present a system for accurate real-time mapping of complex and arbitrary indoor scenes in variable lighting conditions, using only a moving low-cost depth camera and commodity graphics hardware. We fuse all of the depth data streamed from a Kinect sensor into a single global implicit surface model of the observed scene in real-time. The current sensor pose is simultaneously obtained by tracking the live depth frame relative to the global model using a coarse-to-fine iterative closest point (ICP) algorithm, which uses all of the observed depth data available. We demonstrate the advantages of tracking against the growing full surface model compared with frame-to-frame tracking, obtaining tracking and mapping results in constant time within room sized scenes with limited drift and high accuracy. We also show both qualitative and quantitative results relating to various aspects of our tracking and mapping system. Modelling of natural scenes, in real-time with only commodity sensor and GPU hardware, promises an exciting step forward in augmented reality (AR), in particular, it allows dense surfaces to be reconstructed in real-time, with a level of detail and robustness beyond any solution yet presented using passive computer vision.