Volumetric Representation and Manipulation of Geometric Models

Turk, Greg; Nooruddin, F. S.; O’Brien, James F.; Yngve, Gary

doi:10.21236/ada389494

Cited by 6 publications

(6 citation statements)

References 4 publications

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“…To assess the proposed algorithm’s efficiency in handling a substantial amount of point cloud data, a model called Armadillo (Turk and Mullins, 2021) and its scanned point cloud are used. The original Armadillo point cloud is down-sampled, and the proposed method is implemented for varying numbers of points: 519, 1,038, 5,189, 10,378 and 20,756.…”

Section: Methodsmentioning

confidence: 99%

Non-rigid point set registration based on Gaussian mixture model with integrated feature divergence

Tang,

Wang,

Chen

et al. 2024

RIA

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Purpose This paper aims to propose a robust method for non-rigid point set registration, using the Gaussian mixture model and accommodating non-rigid transformations. The posterior probabilities of the mixture model are determined through the proposed integrated feature divergence. Design/methodology/approach The method involves an alternating two-step framework, comprising correspondence estimation and subsequent transformation updating. For correspondence estimation, integrated feature divergences including both global and local features, are coupled with deterministic annealing to address the non-convexity problem of registration. For transformation updating, the expectation-maximization iteration scheme is introduced to iteratively refine correspondence and transformation estimation until convergence. Findings The experiments confirm that the proposed registration approach exhibits remarkable robustness on deformation, noise, outliers and occlusion for both 2D and 3D point clouds. Furthermore, the proposed method outperforms existing analogous algorithms in terms of time complexity. Application of stabilizing and securing intermodal containers loaded on ships is performed. The results demonstrate that the proposed registration framework exhibits excellent adaptability for real-scan point clouds, and achieves comparatively superior alignments in a shorter time. Originality/value The integrated feature divergence, involving both global and local information of points, is proven to be an effective indicator for measuring the reliability of point correspondences. This inclusion prevents premature convergence, resulting in more robust registration results for our proposed method. Simultaneously, the total operating time is reduced due to a lower number of iterations.

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Section: Methodsmentioning

confidence: 99%

Non-rigid point set registration based on Gaussian mixture model with integrated feature divergence

Tang,

Wang,

Chen

et al. 2024

RIA

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show abstract

“…Four kinds of multiplate objects (second row of Figure 9: Longitudinal, Fixture, Clip1 and Clip2) are generated by CAD software from us, and three kinds of free-form models (first row of Figure 9: Bunny, Dragon and Buddha) from Turk and Mullins (2021) are chosen as test objects in the experiments, as shown in Figure 9. Note that synthetic point clouds are acquired from simulated scans of the corresponding objects.…”

Section: Methodsmentioning

confidence: 99%

Robust pose estimation for ship block assembly feature based on large-scale scanning

Tang

Chen

Wang

et al. 2023

RIA

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Purpose Hull block assembly is a vital task in ship construction. It is necessary to obtain the actual poses of the assembly features to guide further block alignment. Traditional methods use single-point measurement, which is time-consuming and may lead to loss of key information. Thus, large-scale scanning is introduced for data acquisition, and this paper aims to provide a precise and robust method for retrieving poses based on point set registration. Design/methodology/approach The main problem of point registration is to find the correct transformation between the model and the scene. In this paper, a vote framework based on a new point pair feature is used to calculate the transformation. First, a special edge indicator for multiplate objects is proposed to determine the edges. Subsequently, pair features with an edge description are noted for every point. Finally, a voting scheme based on agglomerative clustering is implemented to determine the optimal transformation. Findings The proposed method not only improves registration efficiency but also maintains high accuracy compared to several commonly used approaches. In particular, for objects composed of plates, the results of pose estimation are more promising because of the compact pair feature. The multiple ship longitudinal localization experiment validates the effectiveness in real scan applications. Originality/value The proposed edge description performs a better detection for the edges of multiplate objects. The pair feature incorporating the edge indicator is more discriminative than the original template, resulting in better robustness to outliers, noise and occlusions.

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“…(1) The comparison with traditional registration methods-DO [42], RDO [11], BCPD [27], LSGCPD [28], and TEASER++ [29] on synthetic datasets (http://visionair.ge.imati.cnr/ (accessed on 25 October 2020)) [48] (in Figure 6a,b) to show the accuracy and robustness of GRDO. (2) The comparison with deep learning registration methods-FMR [18], Deep-RGM [19], RPMNet [20], and RGM [21] on the ModelNet40 datasets [49] (in Figure 6c,d), which involves the selection of a similar point cloud and parameter transfer, and aims to showcase the efficacy of SGRTmreg on the registration of multiple point cloud pairs.…”

Section: Experimentationmentioning

confidence: 99%

SGRTmreg: A Learning-Based Optimization Framework for Multiple Pairwise Registrations

Zhao,

Deng,

Gao

et al. 2024

Sensors

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Point cloud registration is a fundamental task in computer vision and graphics, which is widely used in 3D reconstruction, object tracking, and atlas reconstruction. Learning-based optimization and deep learning methods have been widely developed in pairwise registration due to their own distinctive advantages. Deep learning methods offer greater flexibility and enable registering unseen point clouds that are not trained. Learning-based optimization methods exhibit enhanced robustness and stability when handling registration under various perturbations, such as noise, outliers, and occlusions. To leverage the strengths of both approaches to achieve a less time-consuming, robust, and stable registration for multiple instances, we propose a novel computational framework called SGRTmreg for multiple pairwise registrations in this paper. The SGRTmreg framework utilizes three components—a Searching scheme, a learning-based optimization method called Graph-based Reweighted discriminative optimization (GRDO), and a Transfer module to achieve multi-instance point cloud registration.Given a collection of instances to be matched, a template as a target point cloud, and an instance as a source point cloud, the searching scheme selects one point cloud from the collection that closely resembles the source. GRDO then learns a sequence of regressors by aligning the source to the target, while the transfer module stores and applies the learned regressors to align the selected point cloud to the target and estimate the transformation of the selected point cloud. In short, SGRTmreg harnesses a shared sequence of regressors to register multiple point clouds to a target point cloud. We conduct extensive registration experiments on various datasets to evaluate the proposed framework. The experimental results demonstrate that SGRTmreg achieves multiple pairwise registrations with higher accuracy, robustness, and stability than the state-of-the-art deep learning and traditional registration methods.

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Volumetric Representation and Manipulation of Geometric Models

Cited by 6 publications

References 4 publications

Non-rigid point set registration based on Gaussian mixture model with integrated feature divergence

Non-rigid point set registration based on Gaussian mixture model with integrated feature divergence

Robust pose estimation for ship block assembly feature based on large-scale scanning

SGRTmreg: A Learning-Based Optimization Framework for Multiple Pairwise Registrations

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