Automatic 3-D Modeling of Textured Cultural Heritage Objects

Andreetto, Marco; Brusco, N.; Cortelazzo, G.M.

doi:10.1109/tip.2003.821351

Cited by 47 publications

(24 citation statements)

References 62 publications

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“…There is great focus on automating the registration process to boost the use of 3D models by the conservation community [80,81]. [80] describes a fully automatic registration pipeline for 3D models of cultural heritage objects which takes into account both shape and texture information and is thus particularly useful for symmetrical objects with a distinct texture, such as painted vases.…”

Section: From 3d Acquisitions To a 3d Modelmentioning

confidence: 99%

“…[80] describes a fully automatic registration pipeline for 3D models of cultural heritage objects which takes into account both shape and texture information and is thus particularly useful for symmetrical objects with a distinct texture, such as painted vases. When scanning small objects, the registration process can be easily sped up by placing the object on a turntable and relying on its positioning information, though another technique must be used to register top and bottom views of the object.…”

Section: From 3d Acquisitions To a 3d Modelmentioning

confidence: 99%

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Integration of 3D and multispectral data for cultural heritage applications: Survey and perspectives

Chane

Mansouri

Marzani

et al. 2013

Image and Vision Computing

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Cultural heritage is increasingly put through imaging systems such as multispectral cameras and 3D scanners. Though these acquisition systems are often used independently, they collect complementary information (spectral vs. spatial) used for the study, archiving and visualization of cultural heritage. Recording 3D and multispectral data in a single coordinate system enhances the potential insights in data analysis.We present the state of the art of such acquisition systems and their applications for the study of cultural heritage. We also describe existing registration techniques that can be used to obtain 3D models with multispectral texture and explore the idea of optically tracking acquisition systems to ensure an easy and precise registration.

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Section: From 3d Acquisitions To a 3d Modelmentioning

confidence: 99%

Section: From 3d Acquisitions To a 3d Modelmentioning

confidence: 99%

Integration of 3D and multispectral data for cultural heritage applications: Survey and perspectives

Chane

Mansouri

Marzani

et al. 2013

Image and Vision Computing

View full text Add to dashboard Cite

show abstract

“…Feature extraction and matching (FEM) are widely used for various tasks: object registration [41], modeling [2], retrieval, and recognition [18], as they are applicable to shapes with varying complexities of geometry, varying degrees of overlap, and varying magnitudes of transformation. The SHOT method, based on a signature of histograms of orientations [41], is one of the best methods for the extraction and matching of features from overlapping partial shapes [4,17].…”

Section: Introductionmentioning

confidence: 99%

Accurately estimating rigid transformations in registration using a boosting-inspired mechanism

Liu

Martin

et al. 2016

Pattern Recognition

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Feature extraction and matching provide the basis of many methods for object registration, modeling, retrieval, and recognition. However, this approach typically introduces false matches, due to lack of features, noise, occlusion, and cluttered backgrounds. In registration, these false matches lead to inaccurate estimation of the underlying transformation that brings the overlapping shapes into best possible alignment. In this paper, we propose a novel boosting-inspired method to tackle this challenging task. It includes three key steps: (i) underlying transformation estimation in * Corresponding author. Email: yitian.zhao@bit.edu.cn 1 the weighted least squares sense, (ii) boosting parameter estimation and regularization via Tsallis entropy, and (iii) weight re-estimation and regularization via Shannon entropy and update with a maximum fusion rule. The process is iterated. The final optimal underlying transformation is estimated as a weighted average of the transformations estimated from the latest iterations, with weights given by the boosting parameters. A comparative study based on real shape data shows that the proposed method outperforms four other state-of-the-art methods for evaluating the established point matches, enabling more accurate and stable estimation of the underlying transformation.

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“…Automatic 3D object model reconstruction from multiple registered range images is popular today in applications ranging from object modelling to computer graphics [1,2,9]. 3D object modelling usually involves the following four stages: (1) Scan object surface from various viewpoints; (2) Register the views; (3) Integrate the views; and finally (4) Render the integrated data.…”

Section: Introductionmentioning

confidence: 99%

“…So far, there is no universally stable registration algorithm that can always register any range data accurately. Moreover, the registration error is likely to accumulate continuously with new images added [1]. In this case, integration algorithms are desired to possess a mechanism to compensate these registration errors.…”

Section: Introductionmentioning

confidence: 99%

Incremental Mesh-based Integration of Registered Range Images: Robust to Registration Error and Scanning Noise

Zhou

Liu

2006

Computer Vision – ACCV 2006

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Abstract. Existing integration algorithms often assume that the registration error of neighbouring views is an order of magnitude less than the measurement error [3]. This assumption is very restrictive that automatic registration algorithms can hardly meet. In this paper, we develop a novel integration algorithm, robust to both large registration errors and heavy scanning noise. Firstly, a pre-processing procedure is developed to automatically triangulate a single range image and remove noisy triangles. Secondly, we shift points along their orientations by the projection of their resulting correspondence vectors so that new correspondences can approach together, leading large registration errors to be compensated. Thirdly, overlapping areas between neighbouring views are detected and integrated, considering the confidence of triangles, which is a function of the including angle between the centroid point vector of a triangle and its normal vector. The outcome of integration is a set of disconnected triangles where gaps are caused by the removal of overlapping triangles with low confidence. Fourthly, the disconnected triangles are connected based on the principle of maximizing interior angles. Since the created triangular mesh is not necessarily smooth, finally, we minimize the weighted orientation variation. The experimental results based on real images show that the proposed algorithm significantly outperforms an existing algorithm and is robust to both registration error and scanning noise.

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Automatic 3-D Modeling of Textured Cultural Heritage Objects

Cited by 47 publications

References 62 publications

Integration of 3D and multispectral data for cultural heritage applications: Survey and perspectives

Integration of 3D and multispectral data for cultural heritage applications: Survey and perspectives

Accurately estimating rigid transformations in registration using a boosting-inspired mechanism

Incremental Mesh-based Integration of Registered Range Images: Robust to Registration Error and Scanning Noise

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