![](bg6.png)
(a) Original
Kinect point
cloud.
(b) Interpolated
point cloud.
(c) HD point
cloud generated
by our method.
Figure 10: Verification through interpolation and compar-
ison. The area selected by the red rectangle shows the part
selected for quantitative estimation of the depth variations.
4 CONCLUSIONS
We have presented a methodology which combines
HD resolution images with the low resolution Kinect
to produce high-resolution dense point cloud using
graph cut. Firstly, Kinect and HD cameras are regis-
tered to transfer Kinect point cloud to the HD camera
for obtaining high resolution point cloud space. Then,
we discretize the point cloud in voxel space and for-
mulate a graph cut formulation which take care of the
neighbor smoothness factor. This methodology pro-
duces good high resolution image with the help of low
resolution Kinect point cloud which could be useful in
building high resolution model using Kinect.
ACKNOWLEDGEMENTS
The authors gratefully acknowledge Dr. Subodh Ku-
mar, Neeraj Kulkarni, Kinshuk Sarabhai and Shruti
Agarwal for their constant help in providing several
tools for Kinect data acquisition, module and error
notification respectively.
Authors also acknowledge Department of Science
and Technology, India for sponsoring the project on
“Acquisition, representation, processing and display
of digital heritage sites” with number “RP02362”
under the India Digital Heritage programme which
helped us in acquiring the images at Hampi in Kar-
nataka, India.
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