Global Optimization for Shape Fitting

By Victor S. Lempitsky, Yuri Boykov. 2007

163 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computational Mechanics, Computer Vision and Pattern Recognition

How researchers see this paper

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

In Aerospace Engineering

In Computational Mechanics

In Computer Vision and Pattern Recognition

Citations in the corpus

Learning a Multi-View Stereo Machine (2017, methodology): “These depth maps can then be fused using a variety of different techniques [38, 8, 33, 59, 30].”
Hierarchical Surface Prediction for 3D Object Reconstruction (2017, methodology): “The common methods can be broadly grouped as implicit volumetric reconstruction [7], [18], [19], [22], [38] or explicit mesh based [9], [12] approaches.”
Discrete optimization of ray potentials for semantic 3D reconstruction (2015, neutral): “Regularizing the surface by penalizing the surface area has been proposed in the discrete graph cut formulation [16, 25] and also as continuous convex optimization formulation [28].”
Joint 3D Scene Reconstruction and Class Segmentation (2013, methodology): “Given a collection of depth images (or equivalently densely sampled oriented 3D points) the methods proposed in [13] [27] [23] essentially utilize the surface area as regularization prior, and obtain the final surface representation indirectly via volumetric optimization.”
A Survey of Surface Reconstruction from Point Clouds (2017, methodology): “Existing approaches solve such a formulation via level-set models [Whi98] and graph cuts [LB07].”
Semantic 3D Reconstruction with Continuous Regularization and Ray Potentials Using a Visibility Consistency Constraint (2016, methodology): “To handle the noise in the input data a surface regularization term is added [19, 38].”
An Approximate, Efficient LP Solver for LP Rounding (2013, background): “1 Introduction A host of machine-learning problems can be solved effectively as approximations of such NP-hard combinatorial problems as set cover, set packing, and multiway-cuts [8, 11, 16, 22].”
Learning an interactive segmentation system (2009, methodology): “In addition to the Gaussian Mixture Model (GMM) based color potentials, we also experimented with flux potentials [12] as a second unary term.”
High Accuracy and Visibility-Consistent Dense Multiview Stereo (2012, unknown): “One might wonder if alternatives would not be better suited to this problem, e.g., using the D out subterm for all crossed tetrahedra, which leads to a guided ballooning force [42], [27].”
Multi-Label Semantic 3D Reconstruction Using Voxel Blocks (2016, background): “Standard volumetric 3D reconstruction without any semantics has been explored extensively in computer vision [4, 12, 20, 27].”
Class Specific 3D Object Shape Priors Using Surface Normals (2014, methodology): “Approaches penalizing the surface area have been proposed in the discrete optimization literature [12] and also as continuous convex optimization [22].”
Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid (2013, background): “To allow them to be recombined to reconstruct the surfaces that represent the original objects or scenes [1], these data must be registered.”
Dense Semantic 3D Reconstruction (2017, methodology): “Given a collection of depth images (or equivalently densely sampled oriented 3D points) the methods proposed in [24], [40], [44] essentially utilize the surface area as regularization prior, and obtain the final surface representation indirectly via volumetric optimization.”
Segment based 3D object shape priors (2015, methodology): “Traditionally, a prior that penalizes the surface area is utilized, it has been formulated in both the discrete graph-cut based [14] optimization and the continuous convex optimization literature [25].”