  The modern approaches for computer vision tasks significantly rely on machine
learning, which requires a large number of quality images. While there is a
plethora of image datasets with a single type of images, there is a lack of
datasets collected from multiple cameras. In this thesis, we introduce Paired
Image and Video data from three CAMeraS, namely PIV3CAMS, aimed at multiple
computer vision tasks. The PIV3CAMS dataset consists of 8385 pairs of images
and 82 pairs of videos taken from three different cameras: Canon D5 Mark IV,
Huawei P20, and ZED stereo camera. The dataset includes various indoor and
outdoor scenes from different locations in Zurich (Switzerland) and Cheonan
(South Korea). Some of the computer vision applications that can benefit from
the PIV3CAMS dataset are image/video enhancement, view interpolation, image
matching, and much more. We provide a careful explanation of the data
collection process and detailed analysis of the data. The second part of this
thesis studies the usage of depth information in the view synthesizing task. In
addition to the regeneration of a current state-of-the-art algorithm, we
investigate several proposed alternative models that integrate depth
information geometrically. Through extensive experiments, we show that the
effect of depth is crucial in small view changes. Finally, we apply our model
to the introduced PIV3CAMS dataset to synthesize novel target views as an
example application of PIV3CAMS.
