Fast global registration of 3D sampled surfaces using a multi-z-buffer technique

Benjemaa, Raouf; Schmitt, Francis

doi:10.1016/s0262-8856(98)00115-2

Cited by 82 publications

(42 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This has typically been used as an acceleration method that avoids the expensive closest point finding [1,4,16]. Weik projected individual points onto the other mesh and used intensity and gradient information to find it a better mate nearby [23].…”

Section: Matching and Alignment Methodsmentioning

confidence: 99%

“…Bergevin et al [2] calculate a transformation for each view separately and then apply them simultaneously before the next round of matchings. Benjemaa and Schmitt [1] accelerate the method by applying the new transformation as soon it is calculated. A quite different approach was taken by Stoddart and Hilton [20] and Eggert et al [8].…”

Section: Multiview Registrationmentioning

confidence: 99%

See 1 more Smart Citation

Multiview registration for large data sets

Pulli

Second International Conference on 3-D Digital Imaging and Modeling (Cat. No.PR00062)

414

358

View full text Add to dashboard Cite

In this paper we present a multiview registration method for aligning range data. We first align scans pairwise with each other and use the pairwise alignments as constraints that the multiview step enforces while evenly diffusing the pairwise registration errors. This approach is especially suitable for registering large data sets, since using constraints from pairwise alignments does not require loading the entire data set into memory to perform the alignment. The alignment method is efficient, and it is less likely to get stuck into a local minimum than previous methods, and can be used in conjunction with any pairwise method based on aligning overlapping surface sections.

show abstract

Section: Matching and Alignment Methodsmentioning

confidence: 99%

Section: Multiview Registrationmentioning

confidence: 99%

Multiview registration for large data sets

Pulli

Second International Conference on 3-D Digital Imaging and Modeling (Cat. No.PR00062)

414

358

View full text Add to dashboard Cite

show abstract

“…finding the closest point in the other surface [2][9] [10]. This computation may be accelerated using a k-d tree.…”

Section: Correspondence Matchingmentioning

confidence: 99%

Fast and Robust Image Registration for 3D Face Modeling

2009

2009 Asia-Pacific Conference on Information Processing

View full text Add to dashboard Cite

One method of 3D face modeling is constructing a subspace of 3D face image by a set of persons' 3D face image. It is crucial to register each 3D face image with a reference face image. The current state of the art in image registration is best represented by the well-known iterative closest point (ICP) algorithm and numerous variations on it. Although this method is effective in many domains, it nevertheless suffers from two key limitations: It is not robust to outliers arising from noise and its run time is too long to be applied in real-time systems. To avoid these problems, this paper presents a fast and robust ICP algorithm. The robustness is ensured by improved M-estimator, whose function is defined over a novel residual norm. In addition, because the correspondence matching stage of ICP is usually the one that takes the longest, applications that require ICP to run quickly must choose the matching algorithm with the fastest performance. So we present a fast matching algorithm that has potential application to real-time 3D face model acquisition. Experimental results on 60 persons' 3D scan face data show the improvements realized in this paper.

show abstract

“…Benjemaa [23] proposed a multi-z-buffer technique to accelerate the ICP algorithm. All points are projected in a z-buffer to perform the local search, and they claimed that this space partition speeds up the search for pointto-projection correspondences.…”

Section: Literature Reviewmentioning

confidence: 99%