Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - KinectFusion
T2 - real-time dense surface mapping and tracking
AU - Newcombe, Richard A.
AU - Izadi, Shahram
AU - Hilliges, Otmar
AU - Molyneaux, David
AU - Kim, David
AU - Davison, Andrew J.
AU - Kohli, Pushmeet
AU - Shotton, Jamie
AU - Hodges, Steve
AU - Fitzgibbon, Andrew
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
KW - AR
KW - Dense Reconstruction
KW - Depth Cameras
KW - GPU
KW - Real-Time
KW - SLAM
KW - Tracking
KW - Volumetric Representation
U2 - 10.1109/ISMAR.2011.6092378
DO - 10.1109/ISMAR.2011.6092378
M3 - Conference contribution/Paper
SN - 9781457721830
SP - 127
EP - 136
BT - Mixed and Augmented Reality (ISMAR), 2011 10th IEEE International Symposium on
PB - IEEE Computer Society
CY - Washington, DC, USA
ER -