![](bg7.png)
(a) ‘Desk’ scene with R
rgb
(b) ‘Couch’ scene with R
rgb
(c) ‘Basement’ scene with R
rgb
Figure 7: Meshlab 3D visualization using method R
rgb
.
weights to faces with an additional function to re-
duce ghosting errors at object boundaries. Thirdly,
color histograms are used in combination with sigma-
outlier detectors to remove unreliable generation is
obtained with high details, so that this leads to a
sharper rendering quality. However, from the over-
all low PSNR quality we can conclude that the tex-
tel quality is highly dependent on the 3D modeling
and registration errors. For our scenes, the visual
difference of textel quality between the use of color
histogram and mean color is minimal, because the
average projected area size of a face is rather small
(a few pixels). For such scenes, the benefit of em-
ploying color histograms does not out weight the in-
volved extra computational costs. To improve textel
quality, we could explore the concept of super resolu-
tion (Goldluecke and Cremers, 2009), employ a dis-
tributed weighting scheme (Wang and Kang, 2001) or
perform local matching of the faces (Rocchini et al.,
2002). For improving registration and thereby re-
ducing blurring originated from registration errors,
image-based camera calibration prior to textel gener-
ation is recommended.
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OnMulti-viewTextureMappingofIndoorEnvironmentsusingKinectDepthSensors
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