A neural network scheme for transparent surface modelling
GRAPHITE '05 Proceedings of the 3rd international conference on Computer graphics and interactive techniques in Australasia and South East Asia
Circularly polarized spherical illumination reflectometry
ACM SIGGRAPH Asia 2010 papers
A neural network for simultaneously reconstructing transparent and opaque surfaces
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Estimating surface normals from spherical stokes reflectance fields
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
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In this paper, we propose a novel method to recover thesurface shape of transparent objects. The degree of polarizationof the light reflected from the object surface dependson the reflection angle which, in turn, depends on the object'ssurface normal; thus, by measuring the degree of polarization,we are able to calculate the surface normal of theobject. However, degree of polarization and surface normaldoes not correspond one-to-one, making us to analyze twopolarization images taken from two different view in orderto solve the ambiguity. A parabolic curve will be a strongclue to correspond a point in one image to a point in theother image, where both points represent the same point onobject surface. By comparing the degree of polarization atsuch corresponding points, the true surface normal can bedetermined.