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@ -15,5 +15,6 @@ thesis.out
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thesis.run.xml
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thesis.toc
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thesis.bcf
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pdfa.xmpi
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downloads
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sRGB_IEC61966-2-1_black_scaled.icc
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@ -13,6 +13,8 @@
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type=bsc, % für Bachelorarbeit
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]{tudthesis}
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\usepackage[a-1b]{pdfx}
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\usepackage[T1]{fontenc}
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\usepackage[utf8]{inputenc} % korrekte Darstellung von Umlauten u. Sonderzeichen
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\usepackage[stable]{footmisc} % mehr Optionen für Fußnoten
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@ -88,7 +90,7 @@
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\author{\myname}
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\thesistitle{\mytitleen}{\mytitlede}
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\birthplace{Erbach}
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\date{23.11.2017}
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\date{21.11.2017}
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\referee{\myprof}{M.Sc. Junhwa Hur}
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\department{\myinstitute}
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\group{\myfaculty}
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thesis.xmpdata
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thesis.xmpdata
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% Replace the following information with your document's actual
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% metadata. If you do not want to set a value for a certain parameter,
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% just omit it.
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%
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% Symbols permitted in metadata
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% =============================
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%
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% Within the metadata, all printable ASCII characters except
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% '\', '{', '}', and '%' represent themselves. Also, all printable
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% Unicode characters from the basic multilingual plane (i.e., up to
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% code point U+FFFF) can be used directly with the UTF-8 encoding.
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% Consecutive whitespace characters are combined into a single
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% space. Whitespace after a macro such as \copyright, \backslash, or
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% \sep is ignored. Blank lines are not permitted. Moreover, the
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% following markup can be used:
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%
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% '\ ' - a literal space (for example after a macro)
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% \% - a literal '%'
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% \{ - a literal '{'
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% \} - a literal '}'
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% \backslash - a literal '\'
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% \copyright - the (c) copyright symbol
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%
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% The macro \sep is only permitted within \Author, \Keywords, and
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% \Org. It is used to separate multiple authors, keywords, etc.
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%
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% List of supported metadata fields
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% =================================
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%
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% Here is a complete list of user-definable metadata fields currently
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% supported, and their meanings. More may be added in the future.
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%
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% General information:
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%
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% \Author - the document's human author. Separate multiple
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% authors with \sep.
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% \Title - the document's title.
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% \Keywords - list of keywords, separated with \sep.
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% \Subject - the abstract.
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% \Org - publishers.
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%
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% Copyright information:
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%
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% \Copyright - a copyright statement.
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% \CopyrightURL - location of a web page describing the owner
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% and/or rights statement for this document.
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% \Copyrighted - 'True' if the document is copyrighted, and
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% 'False' if it isn't. This is automatically set
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% to 'True' if either \Copyright or \CopyrightURL
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% is specified, but can be overridden. For
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% example, if the copyright statement is "Public
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% Domain", this should be set to 'False'.
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%
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% Publication information:
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%
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% \PublicationType - The type of publication. If defined, must be
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% one of book, catalog, feed, journal, magazine,
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% manual, newsletter, pamphlet. This is
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% automatically set to "journal" if \Journaltitle
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% is specified, but can be overridden.
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% \Journaltitle - The title of the journal in which the document
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% was published.
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% \Journalnumber - The ISSN for the publication in which the
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% document was published.
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% \Volume - Journal volume.
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% \Issue - Journal issue/number.
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% \Firstpage - First page number of the published version of
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% the document.
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% \Lastpage - Last page number of the published version of
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% the document.
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% \Doi - Digital Object Identifier (DOI) for the
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% document, without the leading "doi:".
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% \CoverDisplayDate - Date on the cover of the journal issue, as a
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% human-readable text string.
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% \CoverDate - Date on the cover of the journal issue, in a
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% format suitable for storing in a database field
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% with a 'date' data type.
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\Title{Motion R-CNN: Instance-level 3D Motion Estimation with Region-based CNNs}
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\Author{Simon Meister}
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\Copyright{Copyright \copyright\ 2017 "Simon Meister"}
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\Keywords{optical flow\sep
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instance segmentation\sep
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deep learning}
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\Subject{With the advent of deep learning, it has become popular to re-purpose generic deep networks for classical
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computer vision problems involving pixel-wise estimation.
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Following this trend, many recent end-to-end deep learning approaches to optical flow and scene flow
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predict complete, high resolution flow fields with a generic network for dense, pixel-wise prediction,
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thereby ignoring the inherent structure of the underlying motion estimation problem and any physical
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constraints within the scene.
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We introduce a scalable end-to-end deep learning approach for dense motion estimation that respects the
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structure of the scene as being composed of distinct objects, thus combining the representation learning
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benefits and speed of end-to-end deep networks with a physically plausible scene model inspired by
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slanted plane energy-minimization approaches to scene flow.
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Building on recent advances in region-based convolutional networks (R-CNNs), we integrate motion
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estimation with instance segmentation. Given two consecutive frames from a monocular RGB-D camera,
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our resulting end-to-end deep network detects objects with precise per-pixel object masks and estimates
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the 3D motion of each detected object between the frames. By additionally estimating a global camera
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motion in the same network, we compose a dense optical flow field based on instance-level and global
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motion predictions. We train our network on the synthetic Virtual KITTI dataset, which provides ground
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truth for all components of our system.}
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\setRGBcolorprofile{sRGB_IEC61966-2-1_black_scaled.icc}
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{sRGB_IEC61966-2-1_black_scaled}
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{sRGB IEC61966 v2.1 with black scaling}
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{http://www.color.org}
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