248 research outputs found
Single-image Tomography: 3D Volumes from 2D Cranial X-Rays
As many different 3D volumes could produce the same 2D x-ray image, inverting
this process is challenging. We show that recent deep learning-based
convolutional neural networks can solve this task. As the main challenge in
learning is the sheer amount of data created when extending the 2D image into a
3D volume, we suggest firstly to learn a coarse, fixed-resolution volume which
is then fused in a second step with the input x-ray into a high-resolution
volume. To train and validate our approach we introduce a new dataset that
comprises of close to half a million computer-simulated 2D x-ray images of 3D
volumes scanned from 175 mammalian species. Applications of our approach
include stereoscopic rendering of legacy x-ray images, re-rendering of x-rays
including changes of illumination, view pose or geometry. Our evaluation
includes comparison to previous tomography work, previous learning methods
using our data, a user study and application to a set of real x-rays
Towards Better Methods of Stereoscopic 3D Media Adjustment and Stylization
Stereoscopic 3D (S3D) media is pervasive in film, photography and art. However, working with
S3D media poses a number of interesting challenges arising from capture and editing. In this thesis
we address several of these challenges. In particular, we address disparity adjustment and present
a layer-based method that can reduce disparity without distorting the scene. Our method was
successfully used to repair several images for the 2014 documentary “Soldiers’ Stories” directed by
Jonathan Kitzen. We then explore consistent and comfortable methods for stylizing stereo images.
Our approach uses a modified version of the layer-based technique used for disparity adjustment
and can be used with a variety of stylization filters, including those in Adobe Photoshop. We
also present a disparity-aware painterly rendering algorithm. A user study concluded that our
layer-based stylization method produced S3D images that were more comfortable than previous
methods. Finally, we address S3D line drawing from S3D photographs. Line drawing is a common
art style that our layer-based method is not able to reproduce. To improve the depth perception of
our line drawings we optionally add stylized shading. An expert survey concluded that our results
were comfortable and reproduced a sense of depth
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