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Intelligent Point Cloud Processing, Sensing and Understanding—Third Edition
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Intelligent Point Cloud Processing, Sensing and Understanding—Third Edition

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

Deadline for manuscript submissions: 10 March 2026 | Viewed by 8428

Special Issue Editor

Dr. Miaohui Wang

E-Mail Website
Guest Editor
School of Information Engineering, Shenzhen University, Shenzhen 518052, China
Interests: video coding; video communication; visual perception; computer vision; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of the previous Special Issues, “Intelligent Point Cloud Processing, Sensing and Understanding” (https://www.mdpi.com/journal/sensors/special_issues/IX18KRFUQ1) and “Intelligent Point Cloud Processing, Sensing and Understanding (Volume II)” (https://www.mdpi.com/journal/sensors/special_issues/ZW695MGH36), we are pleased to announce the next in the series, “Intelligent Point Cloud Processing, Sensing and Understanding—Third Edition”.

Point clouds are deemed to be one of the foundational pillars of representing the 3D digital world, despite irregular topologies among discrete points. Recently, the advancements in sensor technologies that acquire point cloud data for flexible and scalable geometric representation have paved the way for the development of new ideas, methodologies, and solutions in countless remote sensing applications. State-of-the-art sensors are capable of capturing and describing objects in a scene by using dense point clouds from various platforms (satellites, aerial, UAVs, vehicle-borne, backpacks, handheld, and static terrestrial), perspectives (nadir, oblique, and side view), spectra (multispectral), and levels of granularity (point density and completeness). Meanwhile, the ever-expanding application areas of point cloud processing have already covered not only conventional domains in geospatial analysis, but also include manufacturing, civil engineering, construction, transportation, ecology, forestry, and mechanical engineering, amongst others.

This Special Issue aims to include contributions that focus on processing and utilizing point cloud data acquired from laser scanners and other 3D imaging systems. We are particularly interested in original papers that address innovative techniques for generating, handling, and analyzing point cloud data; challenges in dealing with point cloud data in emerging remote sensing applications; and developing new applications for point cloud data.

Dr. Miaohui Wang
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • point cloud acquisition from laser scanners, stereo vision, panoramas, camera phone images and oblique as well as satellite imagery
  • deep learning for point cloud processing
  • point cloud registration, segmentation, object detection, semantic labelling, compression and quality assessment
  • fusion of multimodal point clouds
  • modeling of LiDAR/image-based point cloud processing
  • industrial applications with large-scale point clouds
  • high-performance computing for large-scale point clouds

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Related Special Issue

Published Papers (6 papers)

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Research

23 pages, 14935 KB  
Article
Robust Pedestrian Detection and Intrusion Judgment in Coal Yard Hazard Areas via 3D LiDAR-Based Deep Learning
by Anxin Zhao, Yekai Zhao and Qiuhong Zheng
Sensors 2025, 25(18), 5908; https://doi.org/10.3390/s25185908 - 21 Sep 2025
Viewed by 556
Abstract
Pedestrian intrusion in coal yard work areas is a major cause of accidents, posing challenges for the safe supervision of coal yards. Existing visual detection methods suffer under poor lighting and a lack of 3D data. To overcome these limitations, this study introduces [...] Read more.
Pedestrian intrusion in coal yard work areas is a major cause of accidents, posing challenges for the safe supervision of coal yards. Existing visual detection methods suffer under poor lighting and a lack of 3D data. To overcome these limitations, this study introduces a robust pedestrian intrusion detection method based on 3D LiDAR. Our approach consists of three main components. First, we propose a novel pedestrian detection network called EFT-RCNN. Based on Voxel-RCNN, this network introduces an EnhancedVFE module to improve spatial feature extraction, employs FocalConv to reconstruct the 3D backbone network for enhanced foreground–background distinction, and utilizes TeBEVPooling to optimize bird’s eye view (BEV) generation. Second, a precise 3D hazardous area is defined by combining a polygonal base surface, determined through on-site exploration, with height constraints. Finally, a point–region hierarchical judgment method is designed to calculate the spatial relationship between pedestrians and the hazardous area for graded warning. When evaluated on the public KITTI dataset, the EFT-RCNN network improved the average precision for pedestrian detection by 4.39% in 3D and 4.68% in BEV compared with the baseline, while maintaining a real-time processing speed of 28.56 FPS. In practical tests, the pedestrian detection accuracy reached 92.9%, with an average error in distance measurement of 0.054 m. The experimental results demonstrate that the proposed method effectively mitigates complex environmental interference, enables robust detection, and provides a reliable means for the proactive prevention of pedestrian intrusion accidents. Full article
(This article belongs to the Special Issue Intelligent Point Cloud Processing, Sensing and Understanding—Third Edition)
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23 pages, 11772 KB  
Article
Robust Pose Estimation and Size Classification for Unknown Dump Truck Using Normal Distribution Transform
by Kai Imai, Kota Watanabe, Hiroaki Okabe, Takafumi Matsuyama, Atsushi Shirao and Takuma Ito
Sensors 2025, 25(18), 5701; https://doi.org/10.3390/s25185701 - 12 Sep 2025
Viewed by 424
Abstract
Labor shortage has been a severe problem in the Japanese construction industry, and the automation of construction work has been in high demand. One of the needs is the automation of soil loading onto dump trucks. This task requires pose estimation and size [...] Read more.
Labor shortage has been a severe problem in the Japanese construction industry, and the automation of construction work has been in high demand. One of the needs is the automation of soil loading onto dump trucks. This task requires pose estimation and size classification of the dump trucks to determine the appropriate loading position and volume. At actual construction sites, specifications of dump trucks are not always known in advance. However, most of the existing methods cannot robustly estimate the pose and the size of such unknown dump trucks. To address this issue, we propose a two-stage method that estimates the pose of dump trucks and then classifies their size categories. We use Normal Distribution Transform (NDT) for pose estimation of dump trucks. Specifically, we utilize NDT templates of dump trucks which distinguish global differences among size categories and simultaneously absorb local shape variations within each category. The proposed method is evaluated by data in a real-world environment. The proposed method appropriately estimates the pose of dump trucks under various settings of positions and orientations. In addition, the method correctly classifies the observed dump truck with all three predefined size categories. Furthermore, the computation time is approximately 0.13 s, which is sufficiently short for practical operation. These results indicate that the method will contribute to the automation of soil loading onto dump trucks with unknown specifications. Full article
(This article belongs to the Special Issue Intelligent Point Cloud Processing, Sensing and Understanding—Third Edition)
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20 pages, 5647 KB  
Article
Research on the Improved ICP Algorithm for LiDAR Point Cloud Registration
by Honglei Yuan, Guangyun Li, Li Wang and Xiangfei Li
Sensors 2025, 25(15), 4748; https://doi.org/10.3390/s25154748 - 1 Aug 2025
Cited by 1 | Viewed by 1720
Abstract
Over three decades of research has been undertaken on point cloud registration algorithms, resulting in mature theoretical frameworks and methodologies. However, among the numerous registration techniques used, the impact of point cloud scanning quality on registration outcomes has rarely been addressed. In most [...] Read more.
Over three decades of research has been undertaken on point cloud registration algorithms, resulting in mature theoretical frameworks and methodologies. However, among the numerous registration techniques used, the impact of point cloud scanning quality on registration outcomes has rarely been addressed. In most engineering and industrial measurement applications, the accuracy and density of LiDAR point clouds are highly dependent on laser scanners, leading to significant variability that critically affects registration quality. Key factors influencing point cloud accuracy include scanning distance, incidence angle, and the surface characteristics of the target. Notably, in short-range scanning scenarios, incidence angle emerges as the dominant error source. Building on this insight, this study systematically investigates the relationship between scanning incidence angles and point cloud quality. We propose an incident-angle-dependent weighting function for point cloud observations, and further develop an improved weighted Iterative Closest Point (ICP) registration algorithm. Experimental results demonstrate that the proposed method achieves approximately 30% higher registration accuracy compared to traditional ICP algorithms and a 10% improvement over Faro SCENE’s proprietary solution. Full article
(This article belongs to the Special Issue Intelligent Point Cloud Processing, Sensing and Understanding—Third Edition)
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25 pages, 5526 KB  
Article
Implementation of Integrated Smart Construction Monitoring System Based on Point Cloud Data and IoT Technique
by Ju-Yong Kim, Suhyun Kang, Jungmin Cho, Seungjin Jeong, Sanghee Kim, Youngje Sung, Byoungkil Lee and Gwang-Hee Kim
Sensors 2025, 25(13), 3997; https://doi.org/10.3390/s25133997 - 26 Jun 2025
Viewed by 2414
Abstract
This study presents an integrated smart construction monitoring system that combines point cloud data (PCD) from a 3D laser scanner with real-time IoT sensors and ultra-wideband (UWB) indoor positioning technology to enhance construction site safety and quality management. The system addresses the limitations [...] Read more.
This study presents an integrated smart construction monitoring system that combines point cloud data (PCD) from a 3D laser scanner with real-time IoT sensors and ultra-wideband (UWB) indoor positioning technology to enhance construction site safety and quality management. The system addresses the limitations of traditional BIM-based methods by leveraging high-precision PCD that accurately reflects actual site conditions. Field validation was conducted over 17 days at a residential construction site, focusing on two floors during concrete pouring. The concrete strength prediction model, based on the ASTM C1074 maturity method, achieved prediction accuracy within 1–2 MPa of measured values (e.g., predicted: 26.2 MPa vs. actual: 25.3 MPa at 14 days). The UWB-based worker localization system demonstrated a maximum positioning error of 1.44 m with 1 s update intervals, enabling real-time tracking of worker movements. Static accuracy tests showed localization errors of 0.80–0.94 m under clear line-of-sight and 1.14–1.26 m under partial non-line-of-sight. The integrated platform successfully combined PCD visualization with real-time sensor data, allowing construction managers to monitor concrete curing progress and worker safety simultaneously. Full article
(This article belongs to the Special Issue Intelligent Point Cloud Processing, Sensing and Understanding—Third Edition)
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21 pages, 23041 KB  
Article
An Elastic Fine-Tuning Dual Recurrent Framework for Non-Rigid Point Cloud Registration
by Munan Yuan, Xiru Li and Haibao Tan
Sensors 2025, 25(11), 3525; https://doi.org/10.3390/s25113525 - 3 Jun 2025
Viewed by 681
Abstract
Non-rigid transformation is based on rigid transformation by adding distortions to form a more complex but more consistent common scene. Many advanced non-rigid alignment models are implemented using supervised learning; however, the large number of labels required for the training process makes their [...] Read more.
Non-rigid transformation is based on rigid transformation by adding distortions to form a more complex but more consistent common scene. Many advanced non-rigid alignment models are implemented using supervised learning; however, the large number of labels required for the training process makes their application difficult. Here, an elastic fine-tuning dual recurrent computation for unsupervised non-rigid registration is proposed. At first, we transform a non-rigid transformation into a series of combinations of rigid transformations using an outer recurrent computational network. Then, the inner loop layer computes elastic-controlled rigid incremental transformations by controlling the threshold to obtain a finely coherent rigid transformation. Finally, we design and implement loss functions that constrain deformations and keep transformations as rigid as possible. Extensive experiments validate that the proposed method achieves state-of-the-art performance with 0.01219 earth mover’s distances (EMDs) and 0.0153 root mean square error (RMSE) in non-rigid and rigid scenes, respectively. Full article
(This article belongs to the Special Issue Intelligent Point Cloud Processing, Sensing and Understanding—Third Edition)
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15 pages, 1281 KB  
Article
Robust Human Tracking Using a 3D LiDAR and Point Cloud Projection for Human-Following Robots
by Sora Kitamoto, Yutaka Hiroi, Kenzaburo Miyawaki and Akinori Ito
Sensors 2025, 25(6), 1754; https://doi.org/10.3390/s25061754 - 12 Mar 2025
Viewed by 2121
Abstract
Human tracking is a fundamental technology for mobile robots that work with humans. Various devices are used to observe humans, such as cameras, RGB-D sensors, millimeter-wave radars, and laser range finders (LRF). Typical LRF measurements observe only the surroundings on a particular horizontal [...] Read more.
Human tracking is a fundamental technology for mobile robots that work with humans. Various devices are used to observe humans, such as cameras, RGB-D sensors, millimeter-wave radars, and laser range finders (LRF). Typical LRF measurements observe only the surroundings on a particular horizontal plane. Human recognition using an LRF has a low computational load and is suitable for mobile robots. However, it is vulnerable to variations in human height, potentially leading to detection failures for individuals taller or shorter than the standard height. This work aims to develop a method that is robust to height differences among humans using a 3D LiDAR. We observed the environment using a 3D LiDAR and projected the point cloud onto a single horizontal plane to apply a human-tracking method for 2D LRFs. We investigated the optimal height range of the point clouds for projection and found that using 30% of the point clouds from the top of the measured person provided the most stable tracking. The results of the path-following experiments revealed that the proposed method reduced the proportion of outlier points compared to projecting all the points (from 3.63% to 1.75%). As a result, the proposed method was effective in achieving robust human following. Full article
(This article belongs to the Special Issue Intelligent Point Cloud Processing, Sensing and Understanding—Third Edition)
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