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Remote Sensing
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Recent Trends in 3D Modelling from Point Clouds
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Recent Trends in 3D Modelling from Point Clouds

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

Deadline for manuscript submissions: closed (1 May 2022) | Viewed by 13138

Special Issue Editors

Dr. Wei Yao

E-Mail Website
Guest Editor
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China
Interests: LiDAR; 3D scene perception and analysis; environmental remote sensing; sensor fusion
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hongchao Fan

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, Høgskoleringen 7a, 7491 Trondheim, Norway
Interests: 3D modelling; LiDAR; photogrammetry

Special Issue Information

Dear Colleagues,

Point clouds are deemed to be one of the foundational pillars in representing the 3D digital world, despite their irregular topology among the discrete points associated with them. Recently, advancements in sensor technologies that acquire point cloud data for a flexible and scalable geometric representation have paved the way for the development of new ideas, methodologies, and solutions in countless remote sensing applications. These state-of-the-art sensors can capture and describe objects in a scene using dense point clouds from various platforms (satellite, aerial, UAV, vehicle-borne, backpack, handheld, and static terrestrial), perspectives (nadir, oblique, and side-view), spectra (multispectral), and granularity (point density and completeness). In the last two decades, point clouds generated from images or directly acquired from Lidar (including ALS, MLS, BLS, and TLS) have become the main source for 3D modeling. Many algorithms have since been made available in the form of data-driven, model-driven or hybrid approaches to reconstruct 3D models with semantic information. The latest techniques in deep learning have even made it possible to predict 3D models from point clouds.

The Special Issue aims at 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 which develop new applications for point cloud data. Additionally, we look forward to seeing new algorithms, techniques, and applications of generating 3D city models or digital twins from point cloud data.

Prof. Dr. Wei Yao
Prof. Dr. Hongchao Fan
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. 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 from the laser scanner, stereo vision, panoramas, camera phone images, oblique and satellite imagery
  • Deep learning for point cloud processing
  • Point cloud registration and segmentation
  • Feature extraction, object detection, semantic labeling, and change detection
  • Point cloud processing for indoor modeling and BIM
  • Digital twins from point clouds
  • Semantic city models from 3D point clouds
  • Fusion of multimodal point clouds with imagery for object classification and modeling
  • Modeling urban and natural environment from aerial and mobile LiDAR/image-based point clouds
  • Industrial applications with large-scale point clouds
  • High-performance computing for large-scale point clouds

Published Papers (5 papers)

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Research

22 pages, 1524 KiB  
Article
KdO-Net: Towards Improving the Efficiency of Deep Convolutional Neural Networks Applied in the 3D Pairwise Point Feature Matching
by Rui Zhang, Guangyun Li, Wolfgang Wiedemann and Christoph Holst
Remote Sens. 2022, 14(12), 2883; https://doi.org/10.3390/rs14122883 - 16 Jun 2022
Cited by 3 | Viewed by 1911
Abstract
In this work, we construct a Kd–Octree hybrid index structure to organize the point cloud and generate patch-based feature descriptors at its leaf nodes. We propose a simple yet effective convolutional neural network, termed KdO-Net, with Kd–Octree based descriptors as input for 3D [...] Read more.
In this work, we construct a Kd–Octree hybrid index structure to organize the point cloud and generate patch-based feature descriptors at its leaf nodes. We propose a simple yet effective convolutional neural network, termed KdO-Net, with Kd–Octree based descriptors as input for 3D pairwise point cloud matching. The classic pipeline of 3D point cloud registration involves two steps, viz., the point feature matching and the globally consistent refinement. We focus on the first step that can be further divided into three parts, viz., the key point detection, feature descriptor extraction, and pairwise-point correspondence estimation. In practical applications, the point feature matching is ambiguous and challenging owing to the low overlap of multiple scans, inconsistency of point density, and unstructured properties. To solve these issues, we propose the KdO-Net for 3D pairwise point feature matching and present a novel nearest neighbor searching strategy to address the computation problem. Thereafter, our method is evaluated with respect to an indoor BundleFusion benchmark, and generalized to a challenging outdoor ETH dataset. Further, we have extended our method over our complicated and low-overlapped TUM-lab dataset. The empirical results graphically demonstrate that our method achieves a superior precision and a comparable feature matching recall to the prior state-of-the-art deep learning-based methods, despite the overlap being less than 30 percent. Finally, we implement quantitative and qualitative ablated experiments and visualization interpretations for illustrating the insights and behavior of our network. Full article
(This article belongs to the Special Issue Recent Trends in 3D Modelling from Point Clouds)
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18 pages, 13752 KiB  
Article
Structural Stability Evaluation of Existing Buildings by Reverse Engineering with 3D Laser Scanner
by Arum Jang, Young K. Ju and Min Jae Park
Remote Sens. 2022, 14(10), 2325; https://doi.org/10.3390/rs14102325 - 11 May 2022
Cited by 9 | Viewed by 2915
Abstract
In the Fourth Industrial Revolution, research and development of application technologies that combine high-tech technologies have been actively conducted. Building information modeling (BIM) technology using advanced equipment is considered promising for future construction projects. In particular, using a 3D laser scanner, LIDAR is [...] Read more.
In the Fourth Industrial Revolution, research and development of application technologies that combine high-tech technologies have been actively conducted. Building information modeling (BIM) technology using advanced equipment is considered promising for future construction projects. In particular, using a 3D laser scanner, LIDAR is expected to be a solution for future building safety inspections. This work proposes a new method for evaluating building stability using a 3D laser scanner. In this study, an underground parking lot was analyzed using a 3D laser scanner. Further, structural analysis was performed using the finite element method (FEM) by applying the figure and geometry data acquired from the laser scan. This process includes surveying the modeled point cloud data of the scanned building, such as identifying the relative deflection of the floor slab, and the sectional shape and inclination of the column. Consequently, safety diagnosis was performed using the original evaluation criteria. This confirms that it is precise and efficient to use a 3D laser scanner for building stability assessment. This paper presents a digital point cloud-based approach using a 3D laser scanner to evaluate the stability of buildings. Full article
(This article belongs to the Special Issue Recent Trends in 3D Modelling from Point Clouds)
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31 pages, 17037 KiB  
Article
LiDAR-Based Real-Time Panoptic Segmentation via Spatiotemporal Sequential Data Fusion
by Weiqi Wang, Xiong You, Jian Yang, Mingzhan Su, Lantian Zhang, Zhenkai Yang and Yingcai Kuang
Remote Sens. 2022, 14(8), 1775; https://doi.org/10.3390/rs14081775 - 07 Apr 2022
Cited by 5 | Viewed by 2375
Abstract
Fast and accurate semantic scene understanding is essential for mobile robots to operate in complex environments. An emerging research topic, panoptic segmentation, serves such a purpose by performing the tasks of semantic segmentation and instance segmentation in a unified framework. To improve the [...] Read more.
Fast and accurate semantic scene understanding is essential for mobile robots to operate in complex environments. An emerging research topic, panoptic segmentation, serves such a purpose by performing the tasks of semantic segmentation and instance segmentation in a unified framework. To improve the performance of LiDAR-based real-time panoptic segmentation, this study proposes a spatiotemporal sequential data fusion strategy that fused points in “thing classes” based on accurate data statistics. The data fusion strategy could increase the proportion of valuable data in unbalanced datasets, and thus managed to mitigate the adverse impact of class imbalance in the limited training data. Subsequently, by improving the codec network, the multiscale features shared by semantic and instance branches were efficiently aggregated to achieve accurate panoptic segmentation for each LiDAR scan. Experiments on the publicly available dataset SemanticKITTI showed that our approach could achieve an effective balance between accuracy and efficiency, and it was also applicable to other point cloud segmentation tasks. Full article
(This article belongs to the Special Issue Recent Trends in 3D Modelling from Point Clouds)
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25 pages, 8808 KiB  
Article
IMU-Aided Registration of MLS Point Clouds Using Inertial Trajectory Error Model and Least Squares Optimization
by Zhipeng Chen, Qingquan Li, Jiayuan Li, Dejin Zhang, Jianwei Yu, Yu Yin, Shiwang Lv and Anbang Liang
Remote Sens. 2022, 14(6), 1365; https://doi.org/10.3390/rs14061365 - 11 Mar 2022
Cited by 3 | Viewed by 2594
Abstract
Mobile laser scanning (MLS) point cloud registration plays a critical role in mobile 3D mapping and inspection, but conventional point cloud registration methods for terrain LiDAR scanning (TLS) are not suitable for MLS. To cope with this challenge, we use inertial measurement unit [...] Read more.
Mobile laser scanning (MLS) point cloud registration plays a critical role in mobile 3D mapping and inspection, but conventional point cloud registration methods for terrain LiDAR scanning (TLS) are not suitable for MLS. To cope with this challenge, we use inertial measurement unit (IMU) to assist registration and propose an MLS point cloud registration method based on an inertial trajectory error model. First, we propose an error model of inertial trajectory over a short time period to construct the constraints between trajectory points at different times. On this basis, a relationship between the point cloud registration error and the inertial trajectory error is established, then trajectory error parameters are estimated by minimizing the point cloud registration error using the least squares optimization. Finally, a reliable and concise inertial-assisted MLS registration algorithm is realized. We carried out experiments in three different scenarios: indoor, outdoor and integrated indoor–outdoor. We evaluated the overall performance, accuracy and efficiency of the proposed method. Compared with the ICP method, the accuracy and speed of the proposed method were improved by 2 and 2.8 times, respectively, which verified the effectiveness and reliability of the proposed method. Furthermore, experimental results show the significance of our method in constructing a reliable and scalable mobile 3D mapping system suitable for complex scenes. Full article
(This article belongs to the Special Issue Recent Trends in 3D Modelling from Point Clouds)
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24 pages, 210111 KiB  
Article
The “Fuzzy” Repair of Urban Building Facade Point Cloud Based on Distribution Regularity
by Zijian Zhang, Xiaojun Cheng, Jicang Wu, Lei Zhang, Yanyi Li and Zhenlun Wu
Remote Sens. 2022, 14(5), 1090; https://doi.org/10.3390/rs14051090 - 23 Feb 2022
Cited by 3 | Viewed by 1708
Abstract
The integrity of point cloud is the basis for smoothly ensuring subsequent data processing and application. For “Smart City” and “Scan to Building Information Modeling (BIM)”, complete point cloud data is essential. At present, the most commonly used methods for repairing point cloud [...] Read more.
The integrity of point cloud is the basis for smoothly ensuring subsequent data processing and application. For “Smart City” and “Scan to Building Information Modeling (BIM)”, complete point cloud data is essential. At present, the most commonly used methods for repairing point cloud holes are multi-source data fusion and interpolation. However, these methods either make it difficult to obtain data, or they are ineffective at repairs or labor-intensive. To solve these problems, we proposed a point cloud “fuzzy” repair algorithm based on the distribution regularity of buildings, aiming at the façade of a building in an urban scene, especially for the vehicle Lidar point cloud. First, the point cloud was rotated to be parallel to the plane XOZ, and the feature boundaries of buildings were extracted. These boundaries were further classified as horizontal or vertical. Then, the distance between boundaries was calculated according to the Euclidean distance, and the points were divided into grids based on this distance. Finally, the holes in the grid that needed to be repaired were filled from four adjacent grids by the “copy–paste” method, and the final hole repairs were realized by point cloud smoothing. The quantitative results showed that data integrity improved after the repair and conformed to the state of the building. The angle and position deviation of the repaired grid were less than 0.54° and 3.25 cm, respectively. Compared with human–computer interaction and other methods, our method required less human intervention, and it had high efficiency. This is of promotional significance for the repair and modeling of point cloud in urban buildings. Full article
(This article belongs to the Special Issue Recent Trends in 3D Modelling from Point Clouds)
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