Consistent segmentation of 3D models

Golovinskiy, Aleksey; Funkhouser, Thomas

doi:10.1016/j.cag.2009.03.010

Cited by 176 publications

(150 citation statements)

References 24 publications

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“…Unsupervised co-analysis of shape collections has been an intensely studied problem in recent years [Golovinskiy and Funkhouser 2009;Xu et al 2010;Sidi et al 2011;Huang et al 2011;Hu et al 2012;Xu et al 2012;Kim et al 2013;van Kaick et al 2013;Meng et al 2013;Zheng et al 2014]. The essence of co-analysis is to best utilize a set of shapes to infer correspondences across pairs of member shapes.…”

Section: Co-analysismentioning

confidence: 99%

Deformation-driven topology-varying 3D shape correspondence

Alhashim

Zhuang

et al. 2015

ACM Trans. Graph.

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We present a deformation-driven approach to topology-varying 3D shape correspondence. In this paradigm, the best correspondence between two shapes is the one that results in a minimal-energy, possibly topology-varying, deformation that transforms one shape to conform to the other while respecting the correspondence. Our deformation model, called GeoTopo transform, allows both geometric and topological operations such as part split, duplication, and merging, leading to fine-grained and piecewise continuous correspondence results. The key ingredient of our correspondence scheme is a deformation energy that penalizes geometric distortion, encourages structure preservation, and simultaneously allows topology changes. This is accomplished by connecting shape parts using structural rods, which behave similarly to virtual springs but simultaneously allow the encoding of energies arising from geometric, structural, and topological shape variations. Driven by the combined deformation energy, an optimal shape correspondence is obtained via a pruned beam search. We demonstrate our deformationdriven correspondence scheme on extensive sets of man-made models with rich geometric and topological variation and compare the results to state-of-the-art approaches.

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Section: Co-analysismentioning

confidence: 99%

Deformation-driven topology-varying 3D shape correspondence

Alhashim

Zhuang

et al. 2015

ACM Trans. Graph.

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“…Golovinskiy and Funkhouser [2009] obtain a consistent segmentation of a set by aligning all the shapes and then clustering their primitives. Xu et al [2010] cluster a set of shapes based on a particular shape style in order to synthesize new shapes via style transfer.…”

Section: Co-analysismentioning

confidence: 99%

Co-hierarchical analysis of shape structures

Kaick

Zhang

et al. 2013

ACM Trans. Graph.

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Figure 1: Structural co-hierarchical analysis of a set of velocipedes (bicycles, tricycles and four-cycles). The resulting co-hierarchy (center) is illustrated by a single sample shape from the set, where each node represents a part assembly. Two of the nodes (highlighted in blue and green) are expanded to show the insight gained by the analysis which relates parts with rather different geometries but similar functions. AbstractWe introduce an unsupervised co-hierarchical analysis of a set of shapes, aimed at discovering their hierarchical part structures and revealing relations between geometrically dissimilar yet functionally equivalent shape parts across the set. The core problem is that of representative co-selection. For each shape in the set, one representative hierarchy (tree) is selected from among many possible interpretations of the hierarchical structure of the shape. Collectively, the selected tree representatives maximize the within-cluster structural similarity among them. We develop an iterative algorithm for representative co-selection. At each step, a novel cluster-and-select scheme is applied to a set of candidate trees for all the shapes. The tree-to-tree distance for clustering caters to structural shape analysis by focusing on spatial arrangement of shape parts, rather than their geometric details. The final set of representative trees are unified to form a structural co-hierarchy. We demonstrate co-hierarchical analysis on families of man-made shapes exhibiting high degrees of geometric and finer-scale structural variabilities.

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“…We have also compared the presented technique to the consistent segmentation approaches of Golovinskiy and Funkhouser [2009] and Xu et al [2010]. Representative results are shown in Figures 9 and 10.…”

Section: Analysis Of Segmentationsmentioning

confidence: 99%

“…A related line of work in shape segmentation aims to produce consistent segmentations of multiple shapes, for applications such as part-based 3D modeling [Kraevoy et al 2007;Golovinskiy and Funkhouser 2009;Shapira et al 2010;Xu et al 2010]. Existing approaches to consistent segmentation either make restrictive assumptions on the set of shapes or ensure consistency by means of shape alignment.…”

Section: Introductionmentioning

confidence: 99%

Joint shape segmentation with linear programming

Huang¹,

Koltun²,

Guibas³

2011

Proceedings of the 2011 SIGGRAPH Asia Conference on - SA '11

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We present an approach to segmenting shapes in a heterogenous shape database. Our approach segments the shapes jointly, utilizing features from multiple shapes to improve the segmentation of each. The approach is entirely unsupervised and is based on an integer quadratic programming formulation of the joint segmentation problem. The program optimizes over possible segmentations of individual shapes as well as over possible correspondences between segments from multiple shapes. The integer quadratic program is solved via a linear programming relaxation, using a block coordinate descent procedure that makes the optimization feasible for large databases. We evaluate the presented approach on the Princeton segmentation benchmark and show that joint shape segmentation significantly outperforms single-shape segmentation techniques.

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Consistent segmentation of 3D models

Cited by 176 publications

References 24 publications

Deformation-driven topology-varying 3D shape correspondence

Deformation-driven topology-varying 3D shape correspondence

Co-hierarchical analysis of shape structures

Joint shape segmentation with linear programming

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