High-Pass Quantization for Mesh Encoding

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How researchers see this paper

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

Learning Implicit Fields for Generative Shape Modeling (2018, background): “, [31], have shown that low-frequency displacements (e.”
Recent advances in compression of 3D meshes (2005, unknown): “The latter point has already proven to be of crucial importance for applications related to visualization (see the concept of visual metric in [33, 57]).”
HyperCube: Implicit Field Representations of Voxelized 3D Models (2021, background): “As it was shown in [1, 43] such measures do not describe visual quality.”
ABF++: fast and robust angle based flattening (2005, result): “This is consistent with other recent research [Sorkine et al. 2003; Sumner and Popovic 2004], which consistently indicates that direct solvers outperform the more popular iterative techniques.”
Differential Representations for Mesh Processing (2006, methodology): “Figures 8 and 9 show some quantization results from [26].”
Differential coordinates for interactive mesh editing (2004, background): “[21] derive a geometry compression algorithm that benefits from strong quantization.”
On Linear Variational Surface Deformation Methods (2008, unknown): “Laplacian-based approaches represent the surface by the socalled differential coordinates or Laplacian coordinates [3], [59].”