Recent Advancements in 3D Imaging

A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: 30 April 2025 | Viewed by 1275

Special Issue Editor


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Guest Editor
Technologies of Vision, Fondazione Bruno Kessler, 38123 Trento, Italy
Interests: 3D scene understanding; point cloud registration; unsupervised learning; optimization; large vision language model

Special Issue Information

Dear Colleagues,

The field of 3D imaging is rapidly evolving, with significant advancements that enhance our ability to capture, analyze, display, store, and deliver 3D information. This Special Issue will explore a diverse array of topics within the realm of 3D imaging, encompassing both foundational techniques and novel applications. Key areas of interest include:

  • 3D Image Processing and Analysis Techniques: surface and volume modeling, segmentation, feature detection, morphological operations, reconstruction, texture mapping, and visualization methods.
  • Multimodal Integration and Large Language Models: The integration of 3D imaging with multimodal data sources and the utilization of large language models to enhance image interpretation, automated analysis, and interaction with 3D data.
  • Applications Across Various Domains: The application of 3D imaging in diverse fields such as healthcare (e.g., medical imaging, surgical planning), biometrics, entertainment (e.g., 3D animation, gaming), CAD/CAM and prototyping, architecture, environmental monitoring, and heritage conservation.

The inclusion of multimodal approaches and large language models represents a significant advancement in the 3D imaging domain. By leveraging these technologies, we can achieve more sophisticated analysis, improved user interaction, and enhanced interpretability of 3D data. This Special Issue aims to highlight these synergies and their impact on both research and practical applications.

We invite researchers and practitioners from academia, industry, and related fields to contribute their latest findings and insights. Through this Special Issue, we hope to foster collaboration, drive innovation, and contribute to the ongoing evolution of 3D imaging technologies.

Dr. Guofeng Mei
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • large vision language models
  • multimodal fusion
  • zero shot
  • unsupervised learning
  • 3D imaging techniques
  • transfer learning

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Published Papers (1 paper)

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Research

22 pages, 4066 KiB  
Article
A Specialized Pipeline for Efficient and Reliable 3D Semantic Model Reconstruction of Buildings from Indoor Point Clouds
by Cedrique Fotsing, Willy Carlos Tchuitcheu, Lemopi Isidore Besong, Douglas William Cunningham and Christophe Bobda
J. Imaging 2024, 10(10), 261; https://doi.org/10.3390/jimaging10100261 - 19 Oct 2024
Viewed by 916
Abstract
Recent advances in laser scanning systems have enabled the acquisition of 3D point cloud representations of scenes, revolutionizing the fields of Architecture, Engineering, and Construction (AEC). This paper presents a novel pipeline for the automatic generation of 3D semantic models of multi-level buildings [...] Read more.
Recent advances in laser scanning systems have enabled the acquisition of 3D point cloud representations of scenes, revolutionizing the fields of Architecture, Engineering, and Construction (AEC). This paper presents a novel pipeline for the automatic generation of 3D semantic models of multi-level buildings from indoor point clouds. The architectural components are extracted hierarchically. After segmenting the point clouds into potential building floors, a wall detection process is performed on each floor segment. Then, room, ground, and ceiling extraction are conducted using the walls 2D constellation obtained from the projection of the walls onto the ground plan. The identification of the openings in the walls is performed using a deep learning-based classifier that separates doors and windows from non-consistent holes. Based on the geometric and semantic information from previously detected elements, the final model is generated in IFC format. The effectiveness and reliability of the proposed pipeline are demonstrated through extensive experiments and visual inspections. The results reveal high precision and recall values in the extraction of architectural elements, ensuring the fidelity of the generated models. In addition, the pipeline’s efficiency and accuracy offer valuable contributions to future advancements in point cloud processing. Full article
(This article belongs to the Special Issue Recent Advancements in 3D Imaging)
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