Cross-time registration of 3D point clouds

Saiti, Evdokia; Danelakis, Antonios; Theoharis, Theoharis

doi:10.1016/j.cag.2021.07.005

Cited by 5 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, our research is focused on the surface alterations due to weathering, where the examined object is assumed to have been uniformly exposed to weather conditions, both spatially and temporally. A framework for cross-time 3D registration is proposed in [19] that copes with big data using a down-sampling scheme that is appropriate for objects exhibiting uniform change over time. The proposed method generally outperforms the state-of-the-art in both accuracy and efficiency (Fig.…”

Section: Cross-time Registrationmentioning

confidence: 99%

Adaption of Imaging Techniques for Monitoring Cultural Heritage Objects

Σιάτου

Παπανικολάου²,

Saiti

2022

Advanced Nondestructive and Structural Techniques for Diagnosis, Redesign and Health Monitoring for the Preservation of Cultura

Self Cite

View full text Add to dashboard Cite

The paper describes the ongoing research on an interdisciplinary approach regarding the technological developments adapted for monitoring CH objects. It covers aspects from data capturing, to data processing and cross-time registration methodologies. The work of three individual projects, that are carried out in the framework of ITN-CHANGE (Horizon 2020, GA 813789) project, are presented. These projects are based on the different backgrounds and expertise of the co authors which, when combined, can cover a wide spectrum of information indispensable for the accurate monitoring of CH objects. The potentiality of 3D Digital Image Correlation (3D DIC) for monitoring in and out of plane displacements as well as advances in Reflectance Transformation Imaging (RTI) for data processing for monitoring specular surfaces, are examined. Computational cross-time and multi-modal registration algorithms are developed for correlating 3D non-registered data over-time. Feasibility studies on mock-ups and simulated data are presented for the validation of the adapted methodologies.

show abstract

Section: Cross-time Registrationmentioning

confidence: 99%

Adaption of Imaging Techniques for Monitoring Cultural Heritage Objects

Σιάτου

Παπανικολάου²,

Saiti

2022

Advanced Nondestructive and Structural Techniques for Diagnosis, Redesign and Health Monitoring for the Preservation of Cultura

Self Cite

View full text Add to dashboard Cite

show abstract

“…Point cloud registration has been actively studied in computer vision and graphics [1][2][3][4][5][6], and most studies mainly focus on pairwise registration [7]. The primary objective of pairwise registration is to estimate the transformation parameters that align a source point cloud to a target point cloud.…”

Section: Introductionmentioning

confidence: 99%

“…Given a collection of point clouds, a source point cloud, and a target point cloud, the process for SGRTmreg to achieve registration unfolds in three steps: (1) Selecting a point cloud similar to the source from the collection based on graph structure, coordinates, node importance, and normal vectors via a searching scheme. (2) Learning regressors from the source using the Graph-based Reweighted Discriminative Optimization (GRDO) method by registering the source to the target. GRDO encodes features and learns regressors from key points in graph structures, reducing memory storage and computational costs.…”

Section: Introductionmentioning

confidence: 99%

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

Zhao,

Deng,

Gao

et al. 2024

Sensors

View full text Add to dashboard Cite

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.

show abstract

3D Point Cloud Segmentation Leveraging Global 2D-View Features

Consunji

Liu

2022

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Cross-time registration of 3D point clouds

Cited by 5 publications

References 28 publications

Adaption of Imaging Techniques for Monitoring Cultural Heritage Objects

Adaption of Imaging Techniques for Monitoring Cultural Heritage Objects

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

3D Point Cloud Segmentation Leveraging Global 2D-View Features

Contact Info

Product

Resources

About