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Capturing, Processing, and Synthesizing Surfaces with Details

Sema Berkiten, PhD Thesis, Princeton University, July 2016.


Summary: This thesis focuses on techniques to produce and process detailed geometry including acquisition of real world objects, processing and fusing the captured data, and synthesizing new surfaces from existing ones.


[pdf] [project page]


Learning Detail Transfer based on Geometric Features

Ongoing...




Semi-Automatic Digital Epigraphy from Images with Normals

Sema Berkiten, Xinyi Fan, and Szymon Rusinkiewicz, International Symposium on Non-Photorealistic Animation and Rendering (NPAR), June 2015.


Summary: A semi-automated system for converting photometric datasets (RGB images with normals) into geometry-aware non-photorealistic illustrations that obey the common conventions of epigraphy (black-and-white archaeological drawings of inscriptions).


[pdf] [project page]


Merge2-3D: Combining Multiple Normal Maps with 3D Surfaces

Sema Berkiten, Xinyi Fan, and Szymon Rusinkiewicz, International Conference on 3D Vision (3DV), December 2014.


Summary: Enhancing rough 3D geometry with fine details obtained from unaligned multiple normal maps.


[pdf] [supplemental] [project page]


An RGBN Benchmark

Sema Berkiten and Szymon Rusinkiewicz, Technical Report, Princeton Univeristy.


Summary: A synthetic photometric benchmark for applications such as photometric stereo.


[pdf] [supplemental] [project page]


Alignment of Images Captured Under Different Light Directions

Sema Berkiten and Szymon Rusinkiewicz, Technical Report, Princeton University, July, 2014.


Summary: Alignment of photometric datasets (images of the same object which are captured from a fixed camera position, under different lighting directions).


[pdf] [project page]


A Pointwise Correspondence Based DT-MRI Fiber Similarity Measure

Sema Berkiten and Burak Acar, Proc. 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Buenos Aires, Argentina, IEEE, 2010.


Summary: Clustering 3D Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) fibers.


[pdf] [project page]