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TECHNICAL PAPERS

Reverse Engineering Methods for Digital Restoration Applications

[+] Author and Article Information
Ioana Boier-Martin

 IBM T. J. Watson Research Center, Hawthorne, New York 10532ioana@us.ibm.com

Holly Rushmeier

 Yale University, New Haven, Connecticut 06520holly@acm.org

J. Comput. Inf. Sci. Eng 6(4), 364-371 (May 30, 2006) (8 pages) doi:10.1115/1.2356497 History: Received August 09, 2005; Revised May 30, 2006

In this paper we discuss the challenges of processing and converting 3D scanned data to representations suitable for interactive manipulation in the context of virtual restoration applications. We present a constrained parametrization approach that allows us to represent 3D scanned models as parametric surfaces defined over polyhedral domains. A combination of normal- and spatial-based clustering techniques is used to generate a partition of the model into regions suitable for parametrization. Constraints can be optionally imposed to enforce a strict correspondence between input and output features. We consider two types of virtual restoration methods: (a) a paint restoration method that takes advantage of the normal-based coarse partition to identify large regions of reduced metric distortion suitable for texture mapping and (b) a shape restoration approach that relies on a refined partition used to convert the input model to a multiresolution subdivision representation suitable for intuitive interactive manipulation during digital studies of historical artifacts.

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Copyright © 2006 by American Society of Mechanical Engineers
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Figures

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Figure 1

Top: Akhenaton statue with broken nose (left) and two virtual reconstructions (middle, right). Bottom: underlying parametrization computed using our method and used for interactive shape editing.

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Figure 2

Left to right: input model with textures; geometry shaded without textures: human character detail on the queen’s robe is enlarged for illustration purposes; segmentation using our method produces large height-field patches, well aligned with the main features of the model; previously painted 2D restoration of the character; the painted image is applied to the model by a simple cut-paste-and-blend operation in 2D texture space without the need for 3D intervention

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Figure 3

Virtual paint restoration is needed in different contexts: to remove artifacts introduced by scanning (right) or to study different hypotheses on how the original might have looked like (left)

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Figure 4

Drawback of 3D paint programs that require simplification: severe artifacts appear when the texture is remapped onto the simplified model

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Figure 5

Segmentation steps: (a) Input. (b) Variation of normals across the model. (c) Normal-based clustering and initial placement of generators (black dots). (d) Final partition after spatial-based clustering (using the generators from (c)) and cluster refinement. (e) Coarse polyhedral approximation extracted from the partition in (d). (f) Coarse level of a Catmull-Clark representation after resampling and multiresolution analysis.

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Figure 6

Left to right: constrained segmentation followed by image-based editing (edited regions are shown numbered before and after editing; editing results are marked “E”); before and after comparison for paint restoration; underlying mesh before and after quad parametrization (thick lines indicate patch boundaries)

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Figure 7

Quadrilateral parametrization examples (constraints: indicated by shading)

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Figure 8

Left: naive segmentation of the model in Fig. 2 without constraints results in many regions. Right: the result of our segmentation method.

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Figure 9

Visualization of the face shape numbers across two models

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