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New Perspectives on 3D Point Cloud (Third Edition)
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New Perspectives on 3D Point Cloud (Third Edition)

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 5430

Special Issue Editor

Dr. Sara Gonizzi Barsanti

E-Mail Website
Guest Editor
Department of Engineering, Università Degli Studi Della Campania Luigi Vanvitelli, Via Roma 29, 81031 Aversa, Italy
Interests: photogrammetry; geomatics; surveying; topography; 3D modeling; reverse engineering; finite element analysis; geographic information system; cultural heritage; BIM; HBIM; VR/AR/XR
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Research about 3D point cloud processing and its application areas are expanding not only for geospatial analysis but also in civil engineering and manufacturing, transport and city planning, construction, geology, ecology, forestry, mechanical engineering, and so on. The topics are expanding towards the possibility of classification, shape detection, and data extraction via automated procedures. The process of semantic segmentation and feature extraction can be used for preservation, the prediction of events, climate control, and as a basis for decision making and analysis. Pattern recognition is a hot topic in autonomous vehicles and inspections.

This is a hot topic in research due to its innovation possibilities and multiple potential application fields, not to mention the problems that have not yet found a definitive solution (Poux, F., 2019). Furthermore, the possibility of using and developing open-source applications for data management and processing makes this topic a key area in the research.

This Special Issue will collect original papers regarding the innovative processing and applications of 3D point clouds acquired from remote sensing sensors in different and new areas of research (i.e., forest, environment, cultural heritage, geology, maritime, architecture, climate analysis, and city modeling research). Great importance will be given to new, innovative research regarding open-source solutions, as well as the creation and definition of automated/semi-automated procedures to collect, post-process, classify, and use 3D point clouds.

Topics should be strictly related to the aims of the Remote Sensing journal, with data results from different sensors of 3D reality-based surveys.

The papers suitable for this Special Issue must emphasize new perspectives and innovative methods on 3D point cloud acquisition, analysis, and post-processing, focusing on data extraction, semantic feature extraction, machine and deep learning algorithms, AI, the use of segmented point clouds for HBIM and BIM, and automated and semi-automated procedures. Importance should also be given to multi-temporal point clouds and the characterization of object dynamics.

Dr. Sara Barsanti
Guest Editor

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. Remote Sensing 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 2700 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 (laser scanner, mono/stereo vision, panoramas, phone cameras, aerial and satellite images, indoor and narrow spaces)
  • deep learning for segmentation/semantic representation and processing
  • AI/ML/DL for point cloud processing and pattern recognition
  • NErF
  • monocular depth estimation for 3D point cloud reconstruction
  • voxel creation
  • denoising point clouds
  • data extraction/data analysis/point cloud registration/multi-temporal point clouds
  • change detection
  • from 2D images to 3D Point Clouds with GANs and LLMs

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Published Papers (5 papers)

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Research

23 pages, 3085 KiB  
Article
Remote Sensing of Forest Gap Dynamics in the Białowieża Forest: Comparison of Multitemporal Airborne Laser Scanning and High-Resolution Aerial Imagery Point Clouds
by Miłosz Mielcarek, Sylwia Kurpiewska, Kacper Guderski, Dorota Dobrowolska, Ewa Zin, Łukasz Kuberski, Yousef Erfanifard and Krzysztof Stereńczak
Remote Sens. 2025, 17(7), 1149; https://doi.org/10.3390/rs17071149 - 24 Mar 2025
Viewed by 271
Abstract
Remote sensing technologies like airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) have emerged as efficient tools for detecting and analysing canopy gaps (CGs). Comparing these technologies is essential to determine their functionality and applicability in various environments. Thus, this study aimed [...] Read more.
Remote sensing technologies like airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) have emerged as efficient tools for detecting and analysing canopy gaps (CGs). Comparing these technologies is essential to determine their functionality and applicability in various environments. Thus, this study aimed to assess CG dynamics in the temperate European Białowieża Forest between 2015 and 2022 by comparing ALS data and image-derived point clouds (IPC) from DAP, to evaluate their respective capabilities in describing and analysing forest CG dynamics. Our results demonstrated that ALS-based point clouds provided more detailed and precise spatial information about both the vertical and horizontal structure of forest CGs compared to IPC. ALS detected 27,754 (54%) new CGs between 2015 and 2022, while IPC identified 23,502 (75%) new CGs. Both the average gap area and the total gap area significantly increased over time in both methods. ALS data not only identified a greater number of CGs, particularly smaller ones (below 500 m2), but also produced a more precise representation of CG shape and structure. In conclusion, precise, multi-temporal remote sensing data on the distribution and size of canopy gaps enable effective monitoring of structural changes and disturbances in forest stands, which in turn supports more efficient forest management, e.g., planning of forest regeneration. Full article
(This article belongs to the Special Issue New Perspectives on 3D Point Cloud (Third Edition))
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24 pages, 11349 KiB  
Article
Multi-Size Voxel Cube (MSVC) Algorithm—A Novel Method for Terrain Filtering from Dense Point Clouds Using a Deep Neural Network
by Martin Štroner, Martin Boušek, Jakub Kučera, Hana Váchová and Rudolf Urban
Remote Sens. 2025, 17(4), 615; https://doi.org/10.3390/rs17040615 - 11 Feb 2025
Viewed by 691
Abstract
When filtering highly rugged terrain from dense point clouds (particularly in technical applications such as civil engineering), the most widely used filtering approaches yield suboptimal results. Here, we proposed and tested a novel ground-filtering algorithm, a multi-size voxel cube (MSVC), utilizing a deep [...] Read more.
When filtering highly rugged terrain from dense point clouds (particularly in technical applications such as civil engineering), the most widely used filtering approaches yield suboptimal results. Here, we proposed and tested a novel ground-filtering algorithm, a multi-size voxel cube (MSVC), utilizing a deep neural network. This is based on the voxelization of the point cloud, the classification of individual voxels as ground or non-ground using surrounding voxels (a “voxel cube” of 9 × 9 × 9 voxels), and the gradual reduction in voxel size, allowing the acquisition of custom-level detail and highly rugged terrain from dense point clouds. The MSVC performance on two dense point clouds, capturing highly rugged areas with dense vegetation cover, was compared with that of the widely used cloth simulation filter (CSF) using manually classified terrain as the reference. MSVC consistently outperformed the CSF filter in terms of the correctly identified ground points, correctly identified non-ground points, balanced accuracy, and the F-score. Another advantage of this filter lay in its easy adaptability to any type of terrain, enabled by the utilization of machine learning. The only disadvantage lay in the necessity to manually prepare training data. On the other hand, we aim to account for this in the future by producing neural networks trained for individual landscape types, thus eliminating this phase of the work. Full article
(This article belongs to the Special Issue New Perspectives on 3D Point Cloud (Third Edition))
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20 pages, 8188 KiB  
Article
Structural Analysis and 3D Reconstruction of Underground Pipeline Systems Based on LiDAR Point Clouds
by Qiuyao Lai, Qinchuan Xin, Yuhang Tian, Xiaoyou Chen, Yujie Li and Ruohan Wu
Remote Sens. 2025, 17(2), 341; https://doi.org/10.3390/rs17020341 - 20 Jan 2025
Viewed by 1222
Abstract
The underground pipeline is a critical component of urban water supply and drainage infrastructure. However, the absence of accurate pipe information frequently leads to construction delays and cost overruns, adversely impacting urban management and economic development. To address these challenges, the digital management [...] Read more.
The underground pipeline is a critical component of urban water supply and drainage infrastructure. However, the absence of accurate pipe information frequently leads to construction delays and cost overruns, adversely impacting urban management and economic development. To address these challenges, the digital management of underground pipelines has become essential. Despite its importance, research on the structural analysis and reconstruction of underground pipelines remains limited, primarily due to the complexity of underground environments and the technical constraints of LiDAR technology. This study proposes a framework for reconstructing underground pipelines based on unstructured point cloud data, aiming to accurately identify and reconstruct pipe structures from complex scenes. The Random Sample Consensus (RANSAC) algorithm, enhanced with parameter-adaptive adjustments and subset-independent fitting strategies, is employed to fit centerline segments from the set of center points. These segments were used to reconstruct topological connections, and a Building Information Model (BIM) of the underground pipeline was generated based on the structural analysis. Experiments on actual underground scenes evaluated the method using recall rate, radius error, and deviation between point clouds and models. Results showed an 88.8% recall rate, an average relative radius error below 3%, and a deviation of 3.79 cm, demonstrating the framework’s accuracy. This research provides crucial support for pipeline management and planning in smart city development. Full article
(This article belongs to the Special Issue New Perspectives on 3D Point Cloud (Third Edition))
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30 pages, 63876 KiB  
Article
A Low-Cost 3D Mapping System for Indoor Scenes Based on 2D LiDAR and Monocular Cameras
by Xiaojun Li, Xinrui Li, Guiting Hu, Qi Niu and Luping Xu
Remote Sens. 2024, 16(24), 4712; https://doi.org/10.3390/rs16244712 - 17 Dec 2024
Viewed by 1727
Abstract
The cost of indoor mapping methods based on three-dimensional (3D) LiDAR can be relatively high, and they lack environmental color information, thereby limiting their application scenarios. This study presents an innovative, low-cost, omnidirectional 3D color LiDAR mapping system for indoor environments. The system [...] Read more.
The cost of indoor mapping methods based on three-dimensional (3D) LiDAR can be relatively high, and they lack environmental color information, thereby limiting their application scenarios. This study presents an innovative, low-cost, omnidirectional 3D color LiDAR mapping system for indoor environments. The system consists of two two-dimensional (2D) LiDARs, six monocular cameras, and a servo motor. The point clouds are fused with imagery using a pixel-spatial dual-constrained depth gradient adaptive regularization (PS-DGAR) algorithm to produce dense 3D color point clouds. During fusion, the point cloud is reconstructed inversely based on the predicted pixel depth values, compensating for areas of sparse spatial features. For indoor scene reconstruction, a globally consistent alignment algorithm based on particle filter and iterative closest point (PF-ICP) is proposed, which incorporates adjacent frame registration and global pose optimization to reduce mapping errors. Experimental results demonstrate that the proposed density enhancement method achieves an average error of 1.5 cm, significantly improving the density and geometric integrity of sparse point clouds. The registration algorithm achieves a root mean square error (RMSE) of 0.0217 and a runtime of less than 4 s, both of which outperform traditional iterative closest point (ICP) variants. Furthermore, the proposed low-cost omnidirectional 3D color LiDAR mapping system demonstrates superior measurement accuracy in indoor environments. Full article
(This article belongs to the Special Issue New Perspectives on 3D Point Cloud (Third Edition))
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Graphical abstract

19 pages, 7427 KiB  
Article
Determination of Chimney Non-Verticality from TLS Data Using RANSAC Method
by Žan Pleterski, Gašper Rak and Klemen Kregar
Remote Sens. 2024, 16(23), 4541; https://doi.org/10.3390/rs16234541 - 4 Dec 2024
Viewed by 785
Abstract
The continuous monitoring of tall industrial buildings is necessary to ensure safe operation. With technological advances in terrestrial laser scanning and other non-contact measurement methods, the methods and techniques for assessing the stability of tall industrial chimneys are evolving. This paper presents a [...] Read more.
The continuous monitoring of tall industrial buildings is necessary to ensure safe operation. With technological advances in terrestrial laser scanning and other non-contact measurement methods, the methods and techniques for assessing the stability of tall industrial chimneys are evolving. This paper presents a method for determining the non-verticality and straightness of chimneys that offers significant advantages over existing methods. Narrow bands of scanned point clouds are processed at selected height intervals. Using the RANSAC method, points that do not belong to the chimney shell are filtered and the centre of the circle or ellipse is adjusted using the least squares method. The proposed method enables the efficient filtering of point clouds due to frequent obstructions on the chimney shell, the determination of the regularity of the chimney shell shape, a mathematical analysis of the chimney axis curvature, and an intuitive graphical representation of chimney non-verticality. The comparison of the results with other studies confirms the efficiency of the method. Full article
(This article belongs to the Special Issue New Perspectives on 3D Point Cloud (Third Edition))
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