3D Manikin Face Modeling and Super-Resolution from Range Images
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In this work, a trial of modeling a manikin face using SwissRanger SR-3000 is implemented. The process includes acquiring data, range data restoration, registration and surface reconstruction. Several tests are done to evaluate the camera s performance. Then, the noisy and low-resolution range images are restored by MRF by designing intensity information into the prior so that the restored range measurements obtain the high contrast property of the intensity information. The range images are registered by ICP algorithm. To improve the performance of ICP according to the data, several variants ...
In this work, a trial of modeling a manikin face
using SwissRanger SR-3000 is implemented. The
process includes acquiring data, range data
restoration, registration and surface
reconstruction. Several tests are done to evaluate
the camera s performance. Then, the noisy and low-
resolution range images are restored by MRF by
designing intensity information into the prior so
that the restored range measurements obtain the high
contrast property of the intensity information. The
range images are registered by ICP algorithm. To
improve the performance of ICP according to the
data, several variants are introduced. A new surface
reconstruction and super-resolution algorithm called
2.5D MRF is originated to combine multiple
registered surfaces. This high dimensional MRF
merges surfaces by trying to move locally smooth
patches together and keep the original values for
details. The algorithm is proved to be robust to
noise and registration errors. Finally, a face model
combined by 15 registered views via simple averaging
and super-resolution of the face combined by 3 views
via 2.5D MRF are displayed as the result.
using SwissRanger SR-3000 is implemented. The
process includes acquiring data, range data
restoration, registration and surface
reconstruction. Several tests are done to evaluate
the camera s performance. Then, the noisy and low-
resolution range images are restored by MRF by
designing intensity information into the prior so
that the restored range measurements obtain the high
contrast property of the intensity information. The
range images are registered by ICP algorithm. To
improve the performance of ICP according to the
data, several variants are introduced. A new surface
reconstruction and super-resolution algorithm called
2.5D MRF is originated to combine multiple
registered surfaces. This high dimensional MRF
merges surfaces by trying to move locally smooth
patches together and keep the original values for
details. The algorithm is proved to be robust to
noise and registration errors. Finally, a face model
combined by 15 registered views via simple averaging
and super-resolution of the face combined by 3 views
via 2.5D MRF are displayed as the result.
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