Advancing 3D point cloud understanding through deep transfer learning: A comprehensive survey

Sohail, Shahab Saquib; Himeur, Yassine; Kheddar, Hamza; Amira, Abbes; Fadli, Fodil; Atalla, Shadi; Copiaco, Abigail; Mansoor, Wathiq

doi:10.1016/j.inffus.2024.102601

Information Fusion

2025

DOI: 10.1016/j.inffus.2024.102601

|View full text |Cite

Advancing 3D point cloud understanding through deep transfer learning: A comprehensive survey

Shahab Saquib Sohail,

Yassine Himeur,

Hamza Kheddar

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 179 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Study on Image Classification Algorithm Based on Multi-Scale Feature Fusion and Domain Adaptation

Guo,

Cheng,

Zhang

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

This paper introduces the MMTADAN, an innovative algorithm designed to enhance cross-domain image classification. By integrating multi-scale feature extraction with Taylor series-based detail enhancement and adversarial domain adaptation, the MMTADAN effectively aligns features between the source and target domains. The proposed approach addresses the critical challenge of generalizing classification models across diverse datasets, demonstrating significant improvements in performance. The findings suggest that retaining essential image details through multi-scale extraction and Taylor series enhancement can lead to better classification outcomes, making the MMTADAN a valuable contribution to the field of image classification.

show abstract

Study on Image Classification Algorithm Based on Multi-Scale Feature Fusion and Domain Adaptation

Guo,

Cheng,

Zhang

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

Joint Optimization-Based Texture and Geometry Enhancement Method for Single-Image-Based 3D Content Creation

Park,

Kim,

Kim

et al. 2024

Mathematics

View full text Add to dashboard Cite

Recent studies have explored the generation of three-dimensional (3D) meshes from single images. A key challenge in this area is the difficulty of improving both the generalization and detail simultaneously in 3D mesh generation. To address this issue, existing methods utilize fixed-resolution mesh features to train networks for generalization. This approach is capable of generating the overall 3D shape without limitations on object categories. However, the generated shape often exhibits a blurred surface and suffers from suboptimal texture resolution due to the fixed-resolution mesh features. In this study, we propose a joint optimization method that enhances geometry and texture by integrating generalized 3D mesh generation with adjustable mesh resolution. Specifically, we apply an inverse-rendering-based remeshing technique that enables the estimation of complex-shaped mesh estimations without relying on fixed-resolution structures. After remeshing, we enhance the texture to improve the detailed quality of the remeshed mesh via a texture enhancement diffusion model. By separating the tasks of generalization, detailed geometry estimation, and texture enhancement and adapting different target features for each specific network, the proposed joint optimization method effectively addresses the characteristics of individual objects, resulting in increased surface detail and the generation of high-quality textures. Experimental results on the Google Scanned Objects and ShapeNet datasets demonstrate that the proposed method significantly improves the accuracy of 3D geometry and texture estimation, as evaluated by the PSNR, SSIM, LPIPS, and CD metrics.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Advancing 3D point cloud understanding through deep transfer learning: A comprehensive survey

Cited by 2 publications

References 179 publications

Study on Image Classification Algorithm Based on Multi-Scale Feature Fusion and Domain Adaptation

Study on Image Classification Algorithm Based on Multi-Scale Feature Fusion and Domain Adaptation

Joint Optimization-Based Texture and Geometry Enhancement Method for Single-Image-Based 3D Content Creation

Contact Info

Product

Resources

About