Assembling virtual pots from 3D measurements of their fragments

Cooper, David B.; Orriols, Xavier; Velipasalar, Senem; Vote, Eileen; Joukowsky, Martha Sharp; Kimia, Benjamin B.; Laidlaw, David H.; Mumford, David; Willis, Andrew; Andrews, Stuart; Baker, Jill; Yan, Chao; Han, Duck‐Jong; Kang, Kongbin; Kong, Wei; Leymarie, Frederic Fol

doi:10.1145/585031.585032

Cited by 10 publications

(15 citation statements)

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“…The implemented algorithm uses approximations for doing the minimization, is computationally fast, and is reasonably accurate for the few examples tried. Note, the approximate probability functions to be used in (6) are obtained from the bootstrapping described in §6 : (see [4] for how to specify (6) in terms of the original data points).…”

Section: Pot Reconstructionmentioning

confidence: 99%

Accurately Estimating Sherd 3D Surface Geometry with Application to Pot Reconstruction

Willis

Orriols²,

Cooper

2003

2003 Conference on Computer Vision and Pattern Recognition Workshop

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Archaeological ContextIn antiquity, pots and vases were mass-produced by civilizations for use in a variety of contexts. Hence, pieces of pots and vases, hereafter referred to as sherds, are prevalent artifacts uncovered in many archaeological excavation sites. By studying these fragments, archaeologists obtain great amounts of information about ancient civilizations. This paper provides a highly accurate solution to the difficult problem of extracting a geometric model of the unknown pot structure in the region associated with 3D measurements obtained from a sherd. The model extracted may be used in a variety of applications. Some examples are:shape-based searching of 3D sherd databases; sherd classification; and pot reconstruction. In §7, we touch on the specific application of our models to pot reconstruction. Surfaces of RevolutionThe notation and terminology adopted for surfaces in this paper is that used in classic texts such as [10,7]. A surface of revolution S ∈ 3 is obtained by revolving a planar curve C ∈ 2 about a line l ∈ 3. α is called the profile (or generating) curve and l the axis of S. When the Z-axis is taken as the axis of revolution with profile curve α(z), the surface S may be represented parametrically asWith this parametrization, the curves z = constant are parallels of S and the curves θ = constant are meridians of S. The profile curve characterizes how the radius and height of the surface change for a fixed meridian, see Fig (1). In the literature, [14], an axially symmetric surface is a special case of a Simple Homogeneous Generalized Cylinder, SHGC, one which has circular cross section. In (1), the radius function, r = α(z), is a single-valued function of z. In the case of archaeological sherds, there often occur multiple radius values for a specific height z. Examples include sherds which come from pot bases and rims, see Fig. 3 for

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Section: Pot Reconstructionmentioning

confidence: 99%

Accurately Estimating Sherd 3D Surface Geometry with Application to Pot Reconstruction

Willis

Orriols²,

Cooper

2003

2003 Conference on Computer Vision and Pattern Recognition Workshop

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“…where © is the transformation that produced the best alignment. It can be shown [3] that this cost is equivalent to MLE of the common break-curve on the pot surface for the data points,…”

Section: Sherd Alignment and Geometry Estimationmentioning

confidence: 99%

Bayesian pot-assembly from fragments as problems in perceptual-grouping and geometric-learning

2002

Object Recognition Supported by User Interaction for Service Robots

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A heretofore unsolved problem of great archaeological importance is the automatic assembly of pots made on a wheel from the hundreds (or thousands) of sherds found at an excavation site. An approach is presented to the automatic estimation of mathematical models of such pots from 3D measurements of sherds. A Bayesian approach is formulated beginning with a description of the complete set of geometric parameters that determine the distribution of the sherd measurement data. Matching of fragments and aligning them geometrically into configurations is based on matching break-curves (curves on a pot surface separating fragments), estimated axis and profile curve pairs for individual fragments and configurations of fragments, and a number of features of groups of break-curves. Pot assembly is a bottom-up maximum likelihood performance-based search. Experiments are illustrated on pots which were broken for the purpose, and on sherds from an archaeological dig located in Petra, Jordan. The performance measure can also be aposteriori probability, and many other types of information can be included, e.g., pot wall thickness, surface color, patterns on the surface, etc. This can also be viewed as the problem of learning a geometric object from an unorganized set of free-form fragments of the object and of clutter, or as a problem of perceptual grouping.

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“…Although there are many methods dealing with such challenge, they have some disadvantages nonetheless. For example, in order to make simplification and improve robustness, some authors (Cooper et al 2001;Willis and Cooper 2004;Kampel et al 2002;Razdan et al 2001) imposed the assumption of a global model with known geometry to the original object, i.e. the object is axially symmetric, and the assumption may not be fulfilled in many cases.…”

Section: Introductionmentioning

confidence: 99%

Reassembling 3D Thin Fragments of Unknown Geometry in Cultural Heritage

Zheng

Huang

et al. 2014

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Many fragile antiques had already been broken upon being discovered at archaeology sites. The fragments of these objects cannot be effectively interpreted and studied unless they are successfully reassembled. However, there still exists many problems in the reassembly procedure in existing methods, such as the numerical instabilities of curvature and torsion based methods, the limitation of geometric assumption, and the error accumulation of the pairwise matching approach, etc. Regarding these problems, this paper proposed an approach to match the fragments to each other for their original 3D reconstruction. Instead of the curvatures and torsions, the approach is based on establishing a local Cartesian coordinate at every point of the 3D contour curves. First of all, the 3D meshes of the fragments are acquired by a structure-light based method, with the corresponding 3D contour curves extracted from the outer boundaries. Then, the contour curves are matched and aligned to each other by estimating all the possible 3D rigid transformations of the curve pairs with our defined local Cartesian coordinates, and then the maximum likelihood rigid transformations are selected. Finally, a global refinement is introduced to adjust the alignment errors and improve the final reassembling accuracy. In addition, experiments with two groups of fragments suggest that this approach cannot only match and align fragments effectively, but also improve the accuracy significantly. Comparing with the original 3D model acquired before being broken, the final reassembling accuracy reaches 0.47 mm.

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Assembling virtual pots from 3D measurements of their fragments

Cited by 10 publications

References 0 publications

Accurately Estimating Sherd 3D Surface Geometry with Application to Pot Reconstruction

Accurately Estimating Sherd 3D Surface Geometry with Application to Pot Reconstruction

Bayesian pot-assembly from fragments as problems in perceptual-grouping and geometric-learning

Reassembling 3D Thin Fragments of Unknown Geometry in Cultural Heritage

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