Feature-based surface parameterization and texture mapping

By Eugene Zhang, Konstantin Mischaikow, Greg Turk. TOGS, 2005

318 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design

View Paper: https://www.semanticscholar.org/paper/7b6b3e44c78c6484407c2979f6f1f3ef5673dee2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Evangelos Kalogerakis
2. Hao Zhang
3. Ye Duan
4. Truc Le
5. Karan Singh
6. Aaron Hertzmann
7. Ayellet Tal
8. Sagi Katz

In Computer Graphics and Computer-Aided Design

1. Evangelos Kalogerakis
2. Hao Zhang
3. Karan Singh
4. Aaron Hertzmann
5. Ayellet Tal
6. Sagi Katz
7. Michela Spagnuolo
8. Ye Duan

Citations in the corpus

• Unsupervised co-segmentation for 3D shapes using iterative multi-label optimization (2013, methodology): “Considering the variability of the shape library, in this stage we prefer to emphasize the intrinsic shape descriptors, e.g. AGD, and CF, by enlarging the weights w of those descriptors.”
• Segmentation of 3D meshes through spectral clustering (2004, background): “• in mesh parameterization, the segmented mesh patches are preferably flat or they can approximate simple shapes such as cylinders or cones since this facilitates flattening of the patches to reduce stretching [26];”
• Technical Section: Discrete Laplace-Beltrami operators for shape analysis and segmentation (2009, methodology): “If the nodal domains have 0-genus, each chart can be parameterized using an extension of the barycentric coordinates [41,61].”
• Learning 3D mesh segmentation and labeling (2010, methodology): “This yields 60 features. e) Average Geodesic Distance: The Average Geodesic Distance (AGD) function has been used for shape matching [Hilaga et al. 2001; Zhang et al. 2005].”
• Mesh segmentation using feature point and core extraction (2005, methodology): “Many methods use clustering techniques, such as region growing [4, 24], iterative clustering [31], spectral clustering [23], feature point-based clustering [33, 35], and fuzzy clustering with graph cuts [15].”
• Mesh Segmentation - A Comparative Study (2006, background): “Semantics-oriented approaches to shape segmentation have gained a great interest recently in the research community [5, 20, 28, 19, 34, 32, 15, 18, 16, 14, 25], because they can support parametrization or re-meshing schemes, metamorphosis, 3D shape retrieval, skeleton extraction as well as the model”
• Mesh parameterization: theory and practice (2007, unknown): “, 2003] – 15 charts; and (e) [Zhang et al., 2005] – 24 charts.”
• PointGrid: A Deep Network for 3D Shape Understanding (2018, background): “), dihedral angles between triangles [58], curvatures (Gaussian curvature or mean curvature) [30], geodesic distances on a mesh [17, 64], slippage [61], symmetry, convexity, medial axis [33], shape diameter [44] and motion characteristics [43], shape contexts [4], spin images [21], etc.”
• Reeb graphs for shape analysis and applications (2008, methodology): “Application fields related to the use of Reeb graphs are surface analysis and understanding [11,44]; identification of topological quadrangulations [49]; data simplification [39]; animation [59]; human body segmentation [45]; surface parameterization [60,61] (Fig.”