Efficient variants of the ICP algorithm

Rusinkiewicz, Szymon; Levoy, Marc

doi:10.1109/im.2001.924423

Cited by 3,096 publications

(2,246 citation statements)

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“…1 into an transformation g ∈ G that corresponds to the extrinsic factors and a residual transformation r that accounts for the intrinsic shape variability, i.e., τ = g • r, then optimize g and r in a successive or alternating manner (e.g., EM-style approaches). A typical example is the iterative closest points (ICP) algorithms [5,32] for rigid shape matching, which alternates between establishing correspondences given the Euclidean transformation and estimating the Euclidean transformation given the correspondences. Another important example is related to the incorporation of shape priors and will be discussed a bit later.…”

Section: Main Obstacle -Extrinsic Factorsmentioning

confidence: 99%

“…It is a fundamental problem in computer vision, computer graphics, medical image analysis and has been widely investigated in numerous important applications such as 3D surface matching and reconstruction [5,32,12,30,7,21], statistical shape modeling and knowledge-based segmentation [16,15,22,34], feature correspondence and image registration [28,38,1,20], shape similarity and object recognition [2,3,29]. Let S ⊂ R 3 denote a shape 1 .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling Shapes with Higher-Order Graphs: Methodology and Applications

Wang

Zeng

Samaras

et al. 2013

Shape Perception in Human and Computer Vision

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Extrinsic factors such as object pose and camera parameters are a main source of shape variability and pose an obstacle to efficiently solving shape matching and inference. Most existing methods address the influence of extrinsic factors by decomposing the transformation of the source shape (model) into two parts: one corresponding to the extrinsic factors and the other accounting for intra-class variability and noise, which are solved in a successive or alternating manner. In this chapter, we consider a methodology to circumvent the influence of extrinsic factors by exploiting shape properties that are invariant to them. Based on higher-order graph-based models, we implement such a methodology to address various important vision problems, such as non-rigid 3D surface matching and knowledge-based 3D segmentation, in a one-shot optimization scheme. Experimental results demonstrate the superior performance and potential of this type of approach.

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Section: Main Obstacle -Extrinsic Factorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling Shapes with Higher-Order Graphs: Methodology and Applications

Wang

Zeng

Samaras

et al. 2013

Shape Perception in Human and Computer Vision

View full text Add to dashboard Cite

show abstract

“…We used a customized version of the ICP algorithm aimed to handle the presence of extraneous objects. The algorithm has been inspired by the variants presented in (Rusinkiewicz & Levoy, 2001). Fig.…”

Section: Candidate Face Normalizationmentioning

confidence: 99%

Occlusions in Face Recognition: a 3D Approach

Colombo¹,

Cusano²,

Schettini³

2009

State of the Art in Face Recognition

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“…So a normal metric cannot be estimated from the point cloud with acceptable reliability. Therefore, the closest point-to-point distance should be employed as the error metric as described in [12] . To cope with erroneous measurement, simultaneous ordering is adopted.…”

Section: Robust Determination Of Translation and Rotation Parametersmentioning

confidence: 99%