Analytical solutions for sketch-based convolution surface modeling on the GPU

Zhu, Xiaoqiang; Jin, Xiaogang; Liu, Bo; Zhao, Hanli

doi:10.1007/s00371-011-0662-z

Cited by 17 publications

(15 citation statements)

References 21 publications

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“…However, even with the state-of-the-art techniques like [Eitz et al 2012], sketch-based retrieval for realistic inputs is still challenging, thus heavily relying on users to resolve the ambiguity. Little effort has been put into combining sketch-based retrieval with sketch-based modeling for scene construction [Olsen et al 2009;Zhu et al 2012], which raises another problem of how to determine the position, orientation, and scale of each retrieved model given the same sketches used for retrieval. The existing methods [Shin and Igarashi 2007;Lee and Funkhouser 2008] attempt to automate this step of model placement by using heuristic cues (e.g., no penetration, in contact) but still require users to decide the sketching and modeling orders, which are vital to the use of their heuristic cues.…”

Section: Related Workmentioning

confidence: 99%

Sketch2Scene

Kang

et al. 2013

ACM Trans. Graph.

158

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Figure 1: Without any user intervention, our framework automatically turns a freehand sketch drawing depicting multiple scene objects (left) to semantically valid, well arranged scenes of 3D models (right). (The ground and walls were manually added.) AbstractThis work presents Sketch2Scene, a framework that automatically turns a freehand sketch drawing inferring multiple scene objects to semantically valid, well arranged scenes of 3D models. Unlike the existing works on sketch-based search and composition of 3D models, which typically process individual sketched objects one by one, our technique performs co-retrieval and co-placement of 3D relevant models by jointly processing the sketched objects. This is enabled by summarizing functional and spatial relationships among models in a large collection of 3D scenes as structural groups. Our technique greatly reduces the amount of user intervention needed for sketch-based modeling of 3D scenes and fits well into the traditional production pipeline involving concept design followed by 3D modeling. A pilot study indicates that it is promising to use our technique as an alternative but more efficient tool of standard 3D modeling for 3D scene construction.

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Section: Related Workmentioning

confidence: 99%

Sketch2Scene

Kang

et al. 2013

ACM Trans. Graph.

158

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“…Skeleton-based convolution modelling As natural abstracts of shapes, skeletons capture the essential topology of an object in a very compact form, and they can be easily edited [60]. A convolution surface is defined as an iso-surface in a scalar field by convolving a geometric skeleton with a kernel function [9,37].…”

Section: Related Workmentioning

confidence: 99%

“…A convolution surface is defined as an iso-surface in a scalar field by convolving a geometric skeleton with a kernel function [9,37]. Analytical solutions are the best way to reduce the considerable calculation for the convolution integrals [22,[25][26][27]37,47,60,23]. Since convolution surfaces offer a number of advantages such as smoothness, fluidly varying topology, local control, well behaved blends, and simple implementation, they have numerous applications in design, modelling and animation.…”

Section: Related Workmentioning

confidence: 99%

“…Since convolution surfaces offer a number of advantages such as smoothness, fluidly varying topology, local control, well behaved blends, and simple implementation, they have numerous applications in design, modelling and animation. One interesting application is to model sketch-based models [4,3,2,7,49,60] which uses the advantages of the rotundity and smoothness of convolution surfaces. Another important application of convolution surfaces is to simulate deformable surfaces with complex topology, and the imbedded skeletons make it especially suitable for skeleton-based animation or special effects.…”

Section: Related Workmentioning

confidence: 99%

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High-quality tree structures modelling using local convolution surface approximation

2013

Self Cite

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In this paper, we propose a local convolution surface approximation approach for quickly modelling tree structures with pleasing visual effect. Using our proposed local convolution surface approximation, we present a tree modelling scheme to create the structure of a tree with a single high-quality quad-only mesh. Through combining the strengths of the convolution surfaces, subdivision surfaces and GPU, our tree modelling approach achieves high efficiency and good mesh quality. With our method, we first extract the line skeletons of given tree models by contracting the meshes with the Laplace operator. Then we approximate the original tree mesh with a convolution surface based on the extracted skeletons. Next, we tessellate the tree trunks represented by convolution surfaces into quadonly subdivision surfaces with good edge flow along the skeletal directions. We implement the most timeconsuming subdivision and convolution approximation on the GPU with CUDA, and demonstrate applications of our proposed approach in branch editing and tree composition.

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“…Skeleton-based implicit surfaces, have been extensively used to model a variety of smooth organic shapes, such as animals and trees, either by direct skeleton manipulation [43,62,60] or through sketch-based modeling [15,6,61,52], or immersive modeling in virtual environments [63].…”

Section: Introductionmentioning

confidence: 99%

Anisotropic convolution surfaces

Suárez¹,

Hubert²,

Zanni

2019

Computers & Graphics

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Convolution surfaces with 1D skeletons have been limited to close-to-circular normal sections. The new formalism presented here allows for ellipsoidal normal sections. Anisotropy is prescribed on G 1 skeletal curves, chosen as circular splines, by a rotation angle and the three radii of an ellipsoid at each extremity. This lightweight model creates smooth shapes that previously required tweaking the skeleton or supplementing it with 2D pieces. The scale invariance of our formalism achieves excellent radii control and thus lends itself to approximate a variety of shapes. The construction of a scaffold is extended to skeletons with G 1 branches. It projects onto the convolution surface as a quad mesh with skeleton bound edge-flow.

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Analytical solutions for sketch-based convolution surface modeling on the GPU

Cited by 17 publications

References 21 publications

Sketch2Scene

Sketch2Scene

High-quality tree structures modelling using local convolution surface approximation

Anisotropic convolution surfaces

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