Non-rigid Point Set Registration with Global-Local Topology Preservation

Song, Ge; Fan, Guoliang; Ding, Meng

doi:10.1109/cvprw.2014.45

Cited by 65 publications

(58 citation statements)

References 19 publications

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“…Chui and Rangarajan [56] established a general framework for estimating correspondence and transformation, where in a nonrigid case, the transformation is modeled as a TPS function. Alternatively, the coherence point drift (CPD) algorithm [57] uses Gaussian radial basis functions instead of TPS, and it was later improved by using global-local topology constraints [58], [59]. In these formulations, both the rigid and nonrigid cases can be dealt with, but these methods usually throw out the similarity information (e.g., a descriptor) and use only the spatial position of each feature point; hence, the matching performance may probably be degraded.…”

Section: B Feature-based Methodsmentioning

confidence: 99%

“…To this end, we introduce an efficient scheme similar to the LLE algorithm [6], [58], which is proposed as a nonlinear dimensionality reduction method to preserve the local neighborhood structure in a low-dimensional manifold. First, search the K nearest neighbors for each point in X. Denote by W an N × N weight matrix, and enforce W ij = 0 if x j does not belong to the set of neighbors of x i .…”

Section: B Local Geometrical Constraintmentioning

confidence: 99%

See 1 more Smart Citation

Robust Feature Matching for Remote Sensing Image Registration via Locally Linear Transforming

Zhou

Zhao

et al. 2015

IEEE Trans. Geosci. Remote Sensing

434

158

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Feature matching, which refers to establishing reliable correspondence between two sets of features (particularly point features), is a critical prerequisite in feature-based registration. In this paper, we propose a flexible and general algorithm, which is called locally linear transforming (LLT), for both rigid and nonrigid feature matching of remote sensing images. We start by creating a set of putative correspondences based on the feature similarity and then focus on removing outliers from the putative set and estimating the transformation as well. We formulate this as a maximum-likelihood estimation of a Bayesian model with hidden/latent variables indicating whether matches in the putative set are outliers or inliers. To ensure the well-posedness of the problem, we develop a local geometrical constraint that can preserve local structures among neighboring feature points, and it is also robust to a large number of outliers. The problem is solved by using the expectation-maximization algorithm (EM), and the closed-form solutions of both rigid and nonrigid transformations are derived in the maximization step. In the nonrigid case, we model the transformation between images in a reproducing kernel Hilbert space (RKHS), and a sparse approximation is applied to the transformation that reduces the method computation complexity to linearithmic. Extensive experiments on real remote sensing images demonstrate accurate results of LLT, which outperforms current state-of-the-art methods, particularly in the case of severe outliers (even up to 80%).

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Section: B Feature-based Methodsmentioning

confidence: 99%

Section: B Local Geometrical Constraintmentioning

confidence: 99%

Robust Feature Matching for Remote Sensing Image Registration via Locally Linear Transforming

Zhou

Zhao

et al. 2015

IEEE Trans. Geosci. Remote Sensing

434

158

View full text Add to dashboard Cite

show abstract

“…Ma proposed his vector field consensus algorithm in [23]. Ge [24] presented the global-local topology preservation (GLTP) algorithm for non-rigid point set registration.…”

Section: Model Registrationmentioning

confidence: 99%

Maintaining Semantic Information across Generic 3D Model Editing Operations

et al. 2020

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Many of today’s data models for 3D applications, such as City Geography Markup Language (CityGML) or Industry Foundation Classes (IFC) encode rich semantic information in addition to the traditional geometry and materials representation. However, 3D editing techniques fall short of maintaining the semantic information across edit operations if they are not tailored to a specific data model. While semantic information is often lost during edit operations, geometry, UV mappings, and materials are usually maintained. This article presents a data model synchronization method that preserves semantic information across editing operation relying only on geometry, UV mappings, and materials. This enables easy integration of existing and future 3D editing techniques with rich data models. The method links the original data model to the edited geometry using point set registration, recovering the existing information based on spatial and UV search methods, and automatically labels the newly created geometry. An implementation of a Level of Detail 3 (LoD3) building editor for the Virtual Singapore project, based on interactive push-pull and procedural generation of façades, verified the method with 30 common editing tasks. The implementation synchronized changes in the 3D geometry with a CityGML data model and was applied to more than 100 test buildings.

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“…Ge et al [26] presented a similar approach to the previous one, called Global-Local Topology Preservation (GLTP). The main motivation of this work is to handle non-rigid articulated deformations such as those of human movements.…”

Section: Coherent Point Drift Variantsmentioning

confidence: 99%

3D non-rigid registration using color: Color Coherent Point Drift

Saval-Calvo

Azorín-López

Fuster-Guilló

et al. 2018

Computer Vision and Image Understanding

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Research into object deformations using computer vision techniques has been under intense study in recent years. A widely used technique is 3D non-rigid registration to estimate the transformation between two instances of a deforming structure. Despite many previous developments on this topic, it remains a challenging problem. In this paper we propose a novel approach to non-rigid registration combining two data spaces in order to robustly calculate the correspondences and transformation between two data sets. In particular, we use point color as well as 3D location as these are the common outputs of RGB-D cameras. We have propose the Color Coherent Point Drift (CCPD) algorithm (an extension of the CPD method [1]). Evaluation is performed using synthetic and real data. The synthetic data includes easy shapes that allow evaluation of the effect of noise, outliers and missing data. Moreover, an evaluation of realistic figures obtained using Blensor is carried out. Real data acquired using a general purpose Primesense Carmine sensor is used to validate the CCPD for real shapes. For all tests, the proposed method is compared to the original CPD showing better results in registration accuracy in most cases.

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Non-rigid Point Set Registration with Global-Local Topology Preservation

Cited by 65 publications

References 19 publications

Robust Feature Matching for Remote Sensing Image Registration via Locally Linear Transforming

Robust Feature Matching for Remote Sensing Image Registration via Locally Linear Transforming

Maintaining Semantic Information across Generic 3D Model Editing Operations

3D non-rigid registration using color: Color Coherent Point Drift

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