Laplacian surface editing

By Olga Sorkine-Hornung, Daniel Cohen-Or, Yaron Lipman, Marc Alexa, C. Rössl, Hans-Peter Seidel. 2004

1,349 citations recorded by the source corpus.

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In Automotive Engineering

Citations in the corpus

Learning Category-Specific Mesh Reconstruction from Image Collections (2018, background): “In particular, enforcing smooth surface has been extensively studied by the Computer Graphics community [20,25].”
SC-GS: Sparse-Controlled Gaussian Splatting for Editable Dynamic Scenes (2023, positive): “However, instead of relying on the implicit and computationally expensive NeRF-based approach, our method employs an explicit point-based control graph deformation strategy and Gaussian rendering, which is more intuitive and efficient.”
Estimating image depth using shape collections (2014, methodology): “Treating these correspondences as landmark correspondences, we then employ Laplacian deformation [Sorkine et al. 2004] to align I and I i .”
ClothCap (2017, background): “The Laplacian term : In order to keep the garment meshes well behaved we include a Laplacian term (Sorkine 2006; Sorkine et al. 2004).”
Subspace gradient domain mesh deformation (2006, methodology): “The only constraint that has appeared in previous related work is the position constraint [Sorkine et al. 2004], which is a linear constraint .”
Linear rotation-invariant coordinates for meshes (2005, methodology): “In the past years several mesh editing techniques were introduced [Zorin et al. 1997; Kobbelt et al. 1998; Guskov et al. 1999; Lee 1999; Bendels and Klein 2003; Botsch and Kobbelt 2004; Lipman et al. 2004; Sheffer and Kraevoy 2004; Sorkine et al. 2004; Yu et al. 2004; Zayer et al. 2005].”
Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold (2023, background): “Conventionalapproaches[Botsch and Sorkine 2007] typically convert images into meshes, and then deform the mesh subject to geometric constraints such as rigidity [Igarashi et al. 2005; Sorkine and Alexa 2007] and Laplacian smoothness [Lipman et al. 2004, 2005; Sorkine et al. 2004].”
As-rigid-as-possible shape manipulation (2005, methodology): “This step corresponds to the 2D case in Laplacian editing [Sorkine et al. 2004].”
ClipGen: A Deep Generative Model for Clipart Vectorization and Synthesis (2021, background): “path, we expected the Laplacian coordinate [62] of the sample point on the predicted path Si to be the same as that on the target path S i .”
Adaptive 3D Face Reconstruction from Unconstrained Photo Collections (2017, methodology): “As in [60], [62], we note that xjL 1⁄4 njHj, where Hj is the integral of the mean curvature in the Voronoi region of vertex j.”
User-Specific Hand Modeling from Monocular Depth Sequences (2014, background): “Several related deformations have been defined, including Laplacian-based measures [23] and ‘embedded deformation’ [26], and our choice of ARAP is to some extent arbitrary, but exploration of the alternatives is left to future work.”
Differential Representations for Mesh Processing (2006, methodology): “This idea was further explored in [46], where the “coating transfer” tool was introduced: by “peeling” mesh detail via smoothing, one can transfer the details onto another mesh.”
Mesh-based inverse kinematics (2005, methodology): “Other editing methods use intrinsic representations such as Laplacian (also called differential) coordinates [Alexa 2003; Lipman et al. 2004; Sorkine et al. 2004] or pyramid coordinates [Sheffer and Kraevoy 2004].”
Mesh editing with poisson-based gradient field manipulation (2004, methodology): “With additional constraints imposed on admissible local transformations, [Sorkine et al. 2004] proposes such a generalization using Laplacian coordinates.”
NeRF-Editing: Geometry Editing of Neural Radiance Fields (2022, methodology): “Traditional mesh deformation methods are based on Laplacian coordinates [35, 57, 58], Poisson equation [79], and dual Laplacian coordinates [2].”