Non-rigid 3D shape retrieval based on multi-scale graphical image and joint Bayesian

Li, Haohao; Su, Zhixun; Li, Nannan; Liu, Ximin; Wang, Shengfa; Luo, Zhongxuan

doi:10.1016/j.cagd.2020.101910

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…A prominent research area within this field is the recovery of motion scenes and their corresponding parameters from continuous image sequences, commonly referred to as 3D motion vision. While significant advances have been made in the reconstruction of rigid bodies, exploring non-rigid body reconstructions continues to present considerable challenges [3]. Non-rigid 3D image reconstruction seeks to recover a 3D model of non-rigid objects from 2D images captured from multiple viewpoints, employing image processing and computer vision techniques [4].…”

Section: Introductionmentioning

confidence: 99%

A Non-Rigid Three-Dimensional Image Reconstruction Algorithm Based on Deformable Shape Reliability

Chen,

Moqurrab

2024

IEEE Access

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Most reconstruction algorithms for non-rigid three-dimensional (3D) images assume that non-rigidity can be represented as a linear combination of a fixed number of rigid bases. However, this assumption struggles to establish reliable shape functions and initial values for nonlinear and non-rigid motions, decreasing reconstruction reliability. This paper introduces an enhanced-reliability reconstruction algorithm for non-rigid 3D images. Our algorithm models the dynamic non-rigid shape basis as a low-rank matrix composed of image points and depth factors, improving the restoration of non-rigid shape base changes and providing accurate parameters for constructing objective functions. By leveraging manifold alignment and physical continuity constraints, our method optimizes the function structures. Assuming minimal reconstruction error and shape change, we solve for the motion structure parameters and select the key initial shape basis value by minimizing the objective function with the L-M nonlinear optimization method. Our experimental results on 3D image sequence reconstructions demonstrate significant error reduction, underscoring our model's credibility, robust reliability, and minimal re-projection error.

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Section: Introductionmentioning

confidence: 99%

A Non-Rigid Three-Dimensional Image Reconstruction Algorithm Based on Deformable Shape Reliability

Chen,

Moqurrab

2024

IEEE Access

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Depth grid-based local description for 3D point clouds

Sa,

Zhang,

Zhang

et al. 2024

SIViP

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With the rapid development and extensive application of next-generation image processing technologies, the manufacturing industry is increasingly adopting intelligent equipment. In order to meet the demands of high precision and high efficiency production, there has been a growing focus on researching 3D point cloud processing methods that go beyond traditional approaches. A fundamental and crucial challenge in the field of point cloud processing is establishing a point-to-point correspondence mapping between two point clouds, which relies on leveraging the local feature description information inherent in the point cloud.This paper thoroughly investigates novel local description methods based on point cloud processing. It addresses the issue of inadequate descriptive capability and robustness found in existing local description methods. Specifically, this study explores the encoding of point information in the neighborhood space and multi-view projection mapping, proposing a local point cloud description method based on depth grids. This method leverages a local reference frame and establishes a depth grid after obtaining the local reference frame through neighborhood projection and distance weighting. The contribution of neighboring points to the depth of the grid is calculated to obtain the eigenvalues. To enhance efficiency, the calculation of eigenvalues incorporates normalization and multi-view projection techniques. The proposed method is compared and evaluated against various local description methods to verify its effectiveness and accuracy.

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Discriminative Multi-view latent Space learning based on Marginal Fisher Analysis

Liu

2020

2020 8th International Conference on Digital Home (ICDH)

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Non-rigid 3D shape retrieval based on multi-scale graphical image and joint Bayesian

Cited by 3 publications

References 22 publications

A Non-Rigid Three-Dimensional Image Reconstruction Algorithm Based on Deformable Shape Reliability

A Non-Rigid Three-Dimensional Image Reconstruction Algorithm Based on Deformable Shape Reliability

Depth grid-based local description for 3D point clouds

Discriminative Multi-view latent Space learning based on Marginal Fisher Analysis

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