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Advanced Imaging Sensors for Object-Shape Recognition and 3D Reconstruction
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Advanced Imaging Sensors for Object-Shape Recognition and 3D Reconstruction

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 1300

Special Issue Editors

Dr. Anna Morabito

E-Mail Website
Guest Editor
Department of Engineering Innovation, University of Salento, 73100 Lecce, Italy
Interests: feature recognition; reverse engineering; 3D acquisition; virtual prototyping; computational geometry; CAD modeling; shape analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Emanuele Guardiani

E-Mail Website
Guest Editor
Department of Industrial and Information Engineering and Economics, University of L’Aquila, 67040 L'Aquila, Italy
Interests: reverse engineering; digital twin; deep learning methods for computer vision; human pose estimation; digitalized risk assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in imaging sensor technologies and 3D data processing have dramatically enhanced our ability to capture and reconstruct both an object’s shape and its semantic geometric structure.

These innovations enable high-resolution, real-time 3D imaging, which is increasingly critical for applications such as autonomous navigation, human–machine interactions, industrial automation, cultural heritage preservation, and biomedical diagnostics.

This Special Issue aims to bring together original research and comprehensive reviews that showcase the latest breakthroughs in sensor design, 3D data acquisition, data processing techniques, and intelligent reconstruction methods. We strongly encourage contributions that explore either fundamental scientific advances or practical applications in industrial research.

Topics of interest include, but are not limited to, the following:

  • Novel architectures for imaging sensors and embedded sensing systems;
  • AI-driven depth sensing and real-time 3D scene reconstruction;
  • Multimodal and multispectral imaging for enhanced shape recognition;
  • Sensor fusion strategies for robust object detection and spatial understanding;
  • Signal processing algorithms for noise reduction, surface modeling, and mesh generation;
  • Application-focused systems in fields such as robotics, healthcare, industrial inspection, and augmented reality.

Dr. Anna Morabito
Dr. Emanuele Guardiani
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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

  • imaging sensors
  • sensor fusion
  • robust object detection

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Published Papers (2 papers)

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Research

24 pages, 3504 KB  
Article
Thermal4D: Physics-Driven Gaussian Splatting for Dynamic Thermal Scene Reconstruction
by Chonghao Zhong and Chao Xu
Sensors 2026, 26(10), 3041; https://doi.org/10.3390/s26103041 - 12 May 2026
Viewed by 258
Abstract
Dynamic scene reconstruction from thermal infrared imagery remains insufficiently studied due to several inherent challenges, including low texture, low contrast, and radiometric ambiguity. In this paper, we present Thermal4D, a novel framework for reconstructing high-fidelity dynamic 3D scenes using only thermal images, without [...] Read more.
Dynamic scene reconstruction from thermal infrared imagery remains insufficiently studied due to several inherent challenges, including low texture, low contrast, and radiometric ambiguity. In this paper, we present Thermal4D, a novel framework for reconstructing high-fidelity dynamic 3D scenes using only thermal images, without requiring visible-light inputs or auxiliary sensors. Built upon the 3D Gaussian Splatting paradigm, the proposed method introduces two key components. First, a frequency-aware attention module, termed TherHiLo, is designed to disentangle structural features across different frequency bands. Second, a physics-inspired atmospheric transmission module (ATM) is developed to model radiometric distortions caused by thermal imaging conditions. Although the reconstruction pipeline takes 8-bit thermal video sequences as input, high-precision 14-bit thermal frames are further exploited in TherHiLo to enhance attention learning with richer radiometric information. In addition, feature-level supervision from pretrained DINOv2 models is incorporated to improve structural consistency. To facilitate systematic evaluation, we also construct MVTD, a new multi-view dynamic thermal dataset. Experimental results on the MVTD and TI-NSD benchmarks show that Thermal4D consistently outperforms existing methods in both dynamic and static scenes, providing an effective framework for physics-consistent dynamic thermal scene reconstruction. Full article
(This article belongs to the Special Issue Advanced Imaging Sensors for Object-Shape Recognition and 3D Reconstruction)
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20 pages, 37476 KB  
Article
In-Orbit MapAnything: An Enhanced Feed-Forward Metric Framework for 3D Reconstruction of Non-Cooperative Space Targets Under Complex Lighting
by Yinxi Lu, Hongyuan Wang, Qianhao Ning, Ziyang Liu, Yunzhao Zang, Zhen Liao and Zhiqiang Yan
Sensors 2026, 26(7), 2026; https://doi.org/10.3390/s26072026 - 24 Mar 2026
Cited by 1 | Viewed by 643
Abstract
Precise 3D reconstruction of non-cooperative space targets is a prerequisite for active debris removal and on-orbit servicing. However, this task is impeded by severe environmental challenges. Specifically, the limited dynamic range of visible light cameras leads to frequent overexposure or underexposure under extreme [...] Read more.
Precise 3D reconstruction of non-cooperative space targets is a prerequisite for active debris removal and on-orbit servicing. However, this task is impeded by severe environmental challenges. Specifically, the limited dynamic range of visible light cameras leads to frequent overexposure or underexposure under extreme space lighting. Compounded by sparse textures and strong specular reflections, these factors significantly constrain reconstruction accuracy. While existing general-purpose feed-forward models such as MapAnything offer efficient inference, their geometric recovery capabilities degrade sharply when facing significant domain shifts. To address these issues, this paper proposes an enhanced 3D reconstruction framework tailored for the space environment named In-Orbit MapAnything. First, to mitigate data scarcity, we construct a high-quality space target dataset incorporating extreme illumination characteristics, which provides comprehensive auxiliary modalities including accurate camera poses and dense point clouds. Second, we propose the SatMap-Adapter module to mitigate feature degradation caused by severe specular reflections. This architecture employs a hierarchical cascade sampling strategy to align multi-level backbone features and utilizes a lightweight adaptive fusion module to dynamically integrate shallow photometric cues, intermediate structural information, and deep semantic features. Finally, we employ a weight-decomposed low-rank adaptation strategy to achieve parameter-efficient fine-tuning while strictly freezing the pre-trained backbone. Experimental results demonstrate that the proposed method decreases the absolute relative error and Chamfer distance by 15.23% and 20.02% respectively compared to the baseline MapAnything model, while maintaining a rapid inference speed. The proposed approach effectively suppresses reconstruction noise on metallic surfaces and recovers fine geometric structures, validating the effectiveness of our feature-enhanced framework in extreme space environments. Full article
(This article belongs to the Special Issue Advanced Imaging Sensors for Object-Shape Recognition and 3D Reconstruction)
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