Shape Decomposition using Modal Analysis

Huang, Qixing; Wicke, Martin; Adams, Bart; Guibas, Leonidas J.

doi:10.1111/j.1467-8659.2009.01380.x

Cited by 82 publications

(49 citation statements)

References 35 publications

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“…In most cases, the scene is reconstructed as a mesh [19]. Segmentation of meshes has been a subject of much research in computer graphics [14,5,13]. However, most of these methods consider only geometric properties, ignoring the appearance.…”

Section: Introductionmentioning

confidence: 99%

Mesh Based Semantic Modelling for Indoor and Outdoor Scenes

Valentin

Sengupta

Warrell

et al. 2013

2013 IEEE Conference on Computer Vision and Pattern Recognition

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Semantic reconstruction of a scene is important for a variety of applications such as 3D modelling, object recognition and autonomous robotic navigation. However, most object labelling methods work in the image domain and fail to capture the information present in 3D space. In this work we propose a principled way to generate object labelling in 3D. Our method builds a triangulated meshed representation of the scene from multiple depth estimates. We then define a CRF over this mesh, which is able to capture the consistency of geometric properties of the objects present in the scene. In this framework, we are able to generate object hypotheses by combining information from multiple sources: geometric properties (from the 3D mesh), and appearance properties (from images). We demonstrate the robustness of our framework in both indoor and outdoor scenes. For indoor scenes we created an augmented version of the NYU indoor scene dataset (RGB-D images) with object labelled meshes for training and evaluation. For outdoor scenes, we created ground truth object labellings for the KITTI odometry dataset (stereo image sequence). We observe a significant speed-up in the inference stage by performing labelling on the mesh, and additionally achieve higher accuracies.

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Section: Introductionmentioning

confidence: 99%

Mesh Based Semantic Modelling for Indoor and Outdoor Scenes

Valentin

Sengupta

Warrell

et al. 2013

2013 IEEE Conference on Computer Vision and Pattern Recognition

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“…We recursively partition the mesh using k-means clustering, combining geometric position and the modes [Huang et al 2009]. Such clustering strengthens our certificates, because it tends to assign topologically distant mesh regions to different nodes of the BVH, even when geometrically close (see Figure 5).…”

Section: Background: Bvh-based Scdmentioning

confidence: 99%

Subspace self-collision culling

Barbič

James

2010

ACM Trans. Graph.

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We show how to greatly accelerate self-collision detection (SCD) for reduced deformable models. Given a triangle mesh and a set of deformation modes, our method precomputes Subspace SelfCollision Culling (SSCC) certificates which, if satisfied, prove the absence of self-collisions for large parts of the model. At runtime, bounding volume hierarchies augmented with our certificates can aggressively cull overlap tests and reduce hierarchy updates. Our method supports both discrete and continuous SCD, can handle complex geometry, and makes no assumptions about geometric smoothness or normal bounds. It is particularly effective for simulations with modest subspace deformations, where it can often verify the absence of self-collisions in constant time. Our certificates enable low amortized costs, in time and across many objects in multibody dynamics simulations. Finally, SSCC is effective enough to support self-collision tests at audio rates, which we demonstrate by producing the first sound simulations of clattering objects.

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“…An overview of this development can be found in the recent survey by Zhang et al [2010] and in the course notes of a Siggraph Asia 2009 course held by Lévy and Zhang [2009]. In addition, the spectrum of the Hessian of surface deformation energies has been investigated: Huang et al [2009] use eigenmodes of the Hessian of the as-rigid-as-possible energy to construct physically meaningful segmentations of surfaces, and Hildebrandt et al [2010] design surface signatures based on eigenmodes of the Hessian of the discrete shells energy. In general, the vibration modes of a curved surface differ significantly from eigenmodes of the Laplacian.…”

Section: Related Workmentioning

confidence: 99%

Interactive surface modeling using modal analysis

et al. 2011

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We propose a framework for deformation-based surface modeling that is interactive, robust and intuitive to use. The deformations are described by a non-linear optimization problem that models static states of elastic shapes under external forces which implement the user input. Interactive response is achieved by a combination of model reduction, a robust energy approximation, and an efficient quasi-Newton solver. Motivated by the observation that a typical modeling session requires only a fraction of the full shape space of the underlying model, we use second and third derivatives of a deformation energy to construct a low-dimensional shape space that forms the feasible set for the optimization. Based on mesh coarsening, we propose an energy approximation scheme with adjustable approximation quality. The quasi-Newton solver guarantees superlinear convergence without the need of costly Hessian evaluations during modeling. We demonstrate the effectiveness of the approach on different examples including the test suite introduced in [Botsch and Sorkine 2008].

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Shape Decomposition using Modal Analysis

Cited by 82 publications

References 35 publications

Mesh Based Semantic Modelling for Indoor and Outdoor Scenes

Mesh Based Semantic Modelling for Indoor and Outdoor Scenes

Subspace self-collision culling

Interactive surface modeling using modal analysis

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