h1

% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@PHDTHESIS{Gehre:756574,
      author       = {Gehre, Anne},
      othercontributors = {Kobbelt, Leif and Ben-Chen, Mirela},
      title        = {3{D} shape analysis based on feature curve networks},
      volume       = {18},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Aachen},
      reportid     = {RWTH-2019-02557},
      series       = {Selected topics in computer graphics},
      pages        = {1 Online-Ressource (ix, 196 Seiten) : Illustrationen},
      year         = {2019},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University; Dissertation, RWTH Aachen University, 2019},
      abstract     = {For high-level analysis of 3D shapes, we require an
                      abstract representation of geometric data. Typically, this
                      is achieved by developing descriptors on a local pointwise
                      level or globally on the entire shape. Point based
                      descriptors can be very sensitive to local changes of the
                      shape (e.g. noise). In contrast, global descriptors tend to
                      be too coarse, depending on the analysist ask. Hence, a
                      desirable representation encodes different levels of
                      abstraction, which range from a geometric point based
                      description over intermediate topological information to
                      high level structure and is flexible enough to be applied in
                      various shape analysis tasks. In this thesis we show that
                      networks consisting of feature curves are able to capture
                      this information at various levels, and are therefore a
                      well-suited representation for the challenging task of
                      analysis of 3D shapes. Feature curves trace out salient
                      creases and crests of 3D geometric data. Their computation
                      has been studied intensively in the past and various
                      directions such as patch layouts, slippage-based techniques,
                      or crest line tracing have been established. They provide an
                      abstract representation of salient parts of the geometry and
                      contain topological and global structural information about
                      the shape as well as geometric details. E.g., the geometric
                      information can be exploited in low-level geometry
                      processing tasks such as remeshing or smoothing, where
                      geometric entities should align with surface features to
                      obtain high quality output. In contrast, the more high-level
                      structural information contained in the feature curve
                      network provides important cues, which can be beneficial for
                      the analysis of the geometric data. However, automatically
                      computed feature curve networks on raw data can have various
                      defects such as noise, fragmentation, or missing data. To
                      make the feature curves applicable to downstream
                      applications, we require a set of meaningful feature curves
                      with low fragmentation and without misclassified feature
                      curves (e.g. due to noise). First attempts to obtain a more
                      relevant set of curves from the automatically computed
                      feature curve networks resort to local solutions such as
                      smoothing, filtering, or (curvature based) thre sholding of
                      the curves. This however, also removes small-scale or weak
                      feature curves from the network which might describe
                      important details. In this thesis, we combine local (feature
                      curve strength, length, parallelism, etc.) and global
                      (density and symmetry) saliency measures to extract
                      meaningful feature curve networks from raw potentially noisy
                      input networks. While the pure geometric information from
                      these meaningful feature curve networks can beused directly
                      for downstream applications, the more high-level structural
                      information is not aseasily accessible. However, for the
                      analysis of the geometric data, more abstract information is
                      required. Hence, in this thesis we present techniques to
                      obtain structural information from symmetry, reoccurrence,
                      and curve template membership, which can be exploited in
                      shapeanalysis applications. Finally, we present several
                      applications, which benefit from the use of meaningful
                      feature curve networks. These range from low-level geometry
                      processing (remeshing, smoothing) to high-level shape
                      analysis (functional maps).},
      cin          = {122310 / 120000},
      ddc          = {004},
      cid          = {$I:(DE-82)122310_20140620$ / $I:(DE-82)120000_20140620$},
      typ          = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
      doi          = {10.18154/RWTH-2019-02557},
      url          = {https://publications.rwth-aachen.de/record/756574},
}