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KinectFusion: Real-Time Dense Surface Mapping and Tracking
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BibTeX
@MISC{Newcombe_kinectfusion:real-time,
author = {Richard A. Newcombe and Shahram Izadi and Otmar Hilliges and David Molyneaux and David Kim and Andrew J. Davison and Pushmeet Kohli and Jamie Shotton and Steve Hodges and Andrew Fitzgibbon},
title = {KinectFusion: Real-Time Dense Surface Mapping and Tracking },
year = {}
}
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Abstract
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
Keyphrases
real-time dense surface mapping kinect sensor coarse-to-fine iterative closest point low-cost depth camera example output dense reconstruction system observed depth data single global implicit surface model live depth frame mapping result current sensor pose normal map global model full surface model accurate real-time mapping phong-shaded rendering frame-to-frame tracking depth data handheld kinect depth camera arbitrary indoor scene variable lighting condition observed scene commodity graphic hardware
