Least-squares meshes
By Olga Sorkine-Hornung, Daniel Cohen-Or. 2004
233 citations recorded by the source corpus.
Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design
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
- Matthias Nießner
- Angela Dai
- Jiajun Wu
- Andrea Tagliasacchi
- Daniel Cohen-Or
- Josh Tenenbaum
- Hao Zhang
- Charles Ruizhongtai Qi
In Computer Graphics and Computer-Aided Design
- Matthias Nießner
- Angela Dai
- Daniel Cohen-Or
- Andrea Tagliasacchi
- Jiajun Wu
- Olga Sorkine-Hornung
- Hao Zhang
- Josh Tenenbaum
Citations in the corpus
- Learning Shape Priors for Single-View 3D Completion and Reconstruction (2018, methodology): “Traditional methods have attempted to complete shapes with local surface primitives, or to formulate it as an optimization problem [35,44], e.”
- Shape Completion Using 3D-Encoder-Predictor CNNs and Shape Synthesis (2016, methodology): “In particular, filling in small holes has received much attention; for instance, one could fit in local surface primitives, such as planes or quadrics, or address the problem with a continuous energy minimization; e.g., with Laplacian smoothing [38, 25, 47].”
- Curve skeleton extraction from incomplete point cloud (2009, methodology): “is possible using the smoothness prior and via energy minimization in the Laplacian processing framework [Sorkine and Cohen-Or 2004].”
- 3D Object Reconstruction from a Single Depth View with Adversarial Learning (2017, methodology): “Traditional reconstruction approaches typically use interpolation techniques such as plane fitting [51] or Poisson surface estimation [23] [24] to estimate the underlying 3D structure.”
- ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans (2017, methodology): “Traditional approaches, such as Laplacian hole filling [36, 21, 44] or Poisson Surface reconstruction [13, 14] can fill small holes.”
- Least Square Projection: A Fast High-Precision Multidimensional Projection Technique and Its Application to Document Mapping (2008, unknown): “solved is symmetric and sparse, facilitating its solution [ 27 ].”
- Spatial adaption of robot trajectories based on laplacian trajectory editing (2015, background): “The Laplace–Beltrami operator is well-known in the computer graphics community where it is applied to deform (Botsch and Kobbelt 2004a; Sorkine and Cohen-Or 2004); classify (Luxburg 2007; Reuter et al.”
- PCN: Point Completion Network (2018, methodology): “For example, surface reconstruction methods [3, 10, 33, 41, 49, 54, 62] generate smooth interpolations to fill holes in locally incomplete scans.”
- Differential Representations for Mesh Processing (2006, background): “In [24,25], a different class of shape approximation techniques for irregular triangular meshes was introduced.”
- On Linear Variational Surface Deformation Methods (2008, unknown): “This formulation was used to define the so-called Least Squares Meshes [58]—smooth surfaces formed by mesh connectivity and a sparse set of control points with geometry, incorporated by (16).”
- Point Cloud Skeletons via Laplacian Based Contraction (2010, unknown): “Many alternatives are available for the choice of the weighting scheme, such as combinatorial weights [44], mean value weights [47] or cotangent weights [48].”