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文件名称:KinectFusion: Real-Time Dense Surface Mapping and Tracking
文件大小:7.91MB
文件格式:PDF
更新时间:2016-05-28 17:02:45
KinectFusion
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.