Towards locally and globally shape-aware reverse 3D modeling

Goyal, Manish; Murugappan, Sundar; Piya, Cecil; Benjamin, William J.; Fang, Yi; Liu, Min; Ramani, Karthik

doi:10.1016/j.cad.2011.12.004

Cited by 20 publications

(18 citation statements)

References 31 publications

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“…On four models used in [Goyal et al 2012], our decomposition produces 3, 5, 3, and 5 parts, respectively, whereas they obtained 4, 7, 5, and 14 parts, respectively. Table 2: Quantitative comparison between sphere-meshes and skeleton+profile approximations resulting from our decompositions.…”

Section: Comparisonsmentioning

confidence: 84%

“…More relevant is the work of Goyal et al [2012] for GC decomposition, which performs an affinity propagation clustering of crosssectional slices. The key difference is that their clustering scheme is based on a pairwise profile similarity, which serves as the only measure that reflects the goal of finding cylindrical parts.…”

Section: Skeleton-based Segmentationmentioning

confidence: 99%

“…We compare our cover-based decomposition to a well-known clustering-based approach, namely, affinity propagation clustering, which was adopted by Goyal et al [2012] in their GC analysis algorithm. Recall that affinity propagation clustering provides iterative refinement through local message passing.…”

Section: Comparisonsmentioning

confidence: 99%

“…We are not aware of any measure which quantifies the generality of cylindrical primitives or any work which optimizes cylindrical shape decomposition with a global objective. Typically, the extraction of cylindrical parts from a given shape is a by-product of skeletonization, e.g., [Au et al 2008;Reniers et al 2008], via primitive fitting, e.g., [Raab et al 2004;Mortara et al 2004;Attene et al 2006], or based on locally-defined cut or clustering criteria, e.g., [Tagliasacchi et al 2009;Goyal et al 2012].…”

Section: Introductionmentioning

confidence: 99%

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Generalized cylinder decomposition

Zhou¹,

Yin²,

Huang³

et al. 2015

ACM Trans. Graph.

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Decomposing a complex shape into geometrically simple primitives is a fundamental problem in geometry processing. We are interested in a shape decomposition problem where the simple primitives sought are generalized cylinders, which are ubiquitous in both organic forms and man-made artifacts. We introduce a quantitative measure of cylindricity for a shape part and develop a cylindricitydriven optimization algorithm, with a global objective function, for generalized cylinder decomposition. As a measure of geometric simplicity and following the minimum description length principle, cylindricity is defined as the cost of representing a cylinder through skeletal and cross-section profile curves. Our decomposition algorithm progressively builds local to non-local cylinders, which form over-complete covers of the input shape. The over-completeness of the cylinder covers ensures a conservative buildup of the cylindrical parts, leaving the final decision on decomposition to global optimization. We solve the global optimization by finding an exact cover, which optimizes the global objective function. We demonstrate results of our optimal decomposition algorithm on numerous examples and compare with other alternatives.

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Section: Comparisonsmentioning

confidence: 84%

Section: Skeleton-based Segmentationmentioning

confidence: 99%

Section: Comparisonsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Generalized cylinder decomposition

Zhou¹,

Yin²,

Huang³

et al. 2015

ACM Trans. Graph.

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show abstract

“…S HAPE decomposition is a fundamental problem in geometry processing where an arbitrary object is regarded as an arrangement of simple primitives [1], [2] or semantic components [3], [4]. Applications of shape decomposition include disciplines such as object recognition and retrieval [5], [6], shape reconstruction [7], shape clustering [8], or modeling [9].…”

Section: Introductionmentioning

confidence: 99%

Cylindrical Shape Decomposition for 3D Segmentation of Tubular Objects

2021

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We develop a cylindrical shape decomposition (CSD) algorithm to decompose an object, a union of several tubular structures, into its semantic components. We decompose the object using its curve skeleton and restricted translational sweeps. For that, CSD partitions the curve skeleton into maximallength sub-skeletons over an orientation cost, each sub-skeleton corresponds to a semantic component. To find the intersection of the tubular components, CSD translationally sweeps the object in decomposition intervals to identify critical points at which the shape of the object changes substantially. CSD cuts the object at critical points and assigns the same label to parts along the same sub-skeleton, thereby constructing a semantic component. The proposed method further reconstructs the acquired semantic components at the intersection of object parts using generalized cylinders. We apply CSD for segmenting axons in large 3D electron microscopy images and decomposing vascular networks and synthetic objects. We show that our proposal is robust to severe surface noise and outperforms state-of-the-art decomposition techniques in its applications.

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A primitive-based 3D reconstruction method for remanufacturing

Zheng

Liu

et al. 2019

Int J Adv Manuf Technol

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Towards locally and globally shape-aware reverse 3D modeling

Cited by 20 publications

References 31 publications

Generalized cylinder decomposition

Generalized cylinder decomposition

Cylindrical Shape Decomposition for 3D Segmentation of Tubular Objects

A primitive-based 3D reconstruction method for remanufacturing

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