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3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing

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

Deadline for manuscript submissions: closed (19 July 2024) | Viewed by 25431

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. San Jiang

E-Mail Website
Guest Editor
1. Hubei Key Laboratory of Intelligent Geo-Information Processing, School of Computer Sciences, China University of Geosciences, Wuhan 430074, China
2. Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong 999077, China
Interests: image retreival; image matching; structure from motion; multi-view stereo; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Duojie Weng

E-Mail Website
Guest Editor
1. Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong 999077, China
2. Research Institute for Artificial Intelligence of Things, The Hong Kong Polytechnic University, Hong Kong 999077, China
Interests: GNSS; urban planning and navigation; indoor positioning
Special Issues, Collections and Topics in MDPI journals
Dr. Jianchen Liu

E-Mail Website
Guest Editor
The College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
Interests: image matching; bundle adjustment; 3D reconstruction; image based positioning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wanshou Jiang

E-Mail
Guest Editor
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China
Interests: image registering; image classification; change detection; 3D reconstruction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Urban environments are the support platform for the development and evolution of society, economy, and human life. Recently, remote-sensing-based techniques have become a meaningful solution to maintain the orderly evaluation of urban environments. As two critical and complementary roles, 3D reconstruction and mobile mapping are essential to support varying applications in urban environments, including but not limited to automatic driving, smart logistics, pedestrian navigation, and virtual reality. With the rapid evolution of classical techniques, e.g., SfM (Structure from Motion) and SLAM (Simultaneous Localization and Mapping), and the development of cutting-edge techniques, especially related to deep learning, such as NeRF (Neural Radiance Field), recent years have witnessed the explosive development of 3D reconstruction and mobile mapping in urban environments.

This Special Issue focuses on the techniques for 3D reconstruction and mobile mapping in urban environments, especially for new instruments for data acquisitions in complex urban environments, scale-illumination invariant algorithms for robust feature matching, efficient image retrieval for image or LiDAR-based localization, SfM-based solutions for image orientation, SLAM-based solutions for image or LiDAR processing, and deep-learning-based network for feature detection and matching, etc.

In this topic, the involved data sources are limited to the remote sensing field, including images from high altitude satellites, aerial planes, UAVs and MMS vehicles, and point clouds from airborne and ground scanners.

  • new instruments for data acquisitions in complex urban environments
  • scale-illumination invariant algorithms for robust feature matching
  • deep learning for feature detection and matching
  • efficient image retrieval for image or LiDAR-based localization
  • SfM-based solutions for image orientation
  • SLAM-based solutions for image or LiDAR processing
  • Neural Radiance Field for 3D reconstruction
  • high-resolution satellite images for urban building 3D modeling

Dr. San Jiang
Dr. Duojie Weng
Dr. Jianchen Liu
Prof. Dr. Wanshou Jiang
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

  • 3D reconstruction
  • mobile mapping
  • photogrammetry
  • mobile mapping system (MMS)
  • structure from motion (SfM)
  • simultaneous localization and mapping (SLAM)
  • multi-view stereo (MVS)
  • neural radiance field (NeRF)
  • global navigation satellite system (GNSS)
  • light detection and ranging (LiDAR)

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Editorial

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5 pages, 172 KiB  
Editorial
Editorial on Special Issue “3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing”
by San Jiang, Duojie Weng, Jianchen Liu and Wanshou Jiang
Remote Sens. 2024, 16(17), 3236; https://doi.org/10.3390/rs16173236 - 31 Aug 2024
Viewed by 692
Abstract
Both 3D reconstruction and mobile mapping are critical in supporting various applications in urban environments, including but not limited to autonomous driving, smart logistics, pedestrian navigation, and virtual reality [...] Full article
(This article belongs to the Special Issue 3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing)

Research

Jump to: Editorial, Other

25 pages, 14134 KiB  
Article
Fast Robust Point Cloud Registration Based on Compatibility Graph and Accelerated Guided Sampling
by Chengjun Wang, Zhen Zheng, Bingting Zha and Haojie Li
Remote Sens. 2024, 16(15), 2789; https://doi.org/10.3390/rs16152789 - 30 Jul 2024
Cited by 1 | Viewed by 1206
Abstract
Point cloud registration is a crucial technique in photogrammetry, remote sensing, etc. A generalized 3D point cloud registration framework has been developed to estimate the optimal rigid transformation between two point clouds using 3D key point correspondences. However, challenges arise due to the [...] Read more.
Point cloud registration is a crucial technique in photogrammetry, remote sensing, etc. A generalized 3D point cloud registration framework has been developed to estimate the optimal rigid transformation between two point clouds using 3D key point correspondences. However, challenges arise due to the uncertainty in 3D key point detection techniques and the similarity of local surface features. These factors often lead to feature descriptors establishing correspondences containing significant outliers. Current point cloud registration algorithms are typically hindered by these outliers, affecting both their efficiency and accuracy. In this paper, we propose a fast and robust point cloud registration method based on a compatibility graph and accelerated guided sampling. By constructing a compatible graph with correspondences, a minimum subset sampling method combining compatible edge sampling and compatible vertex sampling is proposed to reduce the influence of outliers on the estimation of the registration parameters. Additionally, an accelerated guided sampling strategy based on preference scores is presented, which effectively utilizes model parameters generated during the iterative process to guide the sampling toward inliers, thereby enhancing computational efficiency and the probability of estimating optimal parameters. Experiments are carried out on both synthetic and real-world data. The experimental results demonstrate that our proposed algorithm achieves a significant balance between registration accuracy and efficiency compared to state-of-the-art registration algorithms such as RANSIC and GROR. Even with up to 2000 initial correspondences and an outlier ratio of 99%, our algorithm achieves a minimum rotation error of 0.737° and a minimum translation error of 0.0201 m, completing the registration process within 1 s. Full article
(This article belongs to the Special Issue 3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing)
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22 pages, 11407 KiB  
Article
Research on a Matching Method for Vehicle-Borne Laser Point Cloud and Panoramic Images Based on Occlusion Removal
by Jiashu Ji, Weiwei Wang, Yipeng Ning, Hanwen Bo and Yufei Ren
Remote Sens. 2024, 16(14), 2531; https://doi.org/10.3390/rs16142531 - 10 Jul 2024
Cited by 1 | Viewed by 855
Abstract
Vehicle-borne mobile mapping systems (MMSs) have been proven as an efficient means of photogrammetry and remote sensing, as they simultaneously acquire panoramic images, point clouds, and positional information along the collection route from a ground-based perspective. Obtaining accurate matching results between point clouds [...] Read more.
Vehicle-borne mobile mapping systems (MMSs) have been proven as an efficient means of photogrammetry and remote sensing, as they simultaneously acquire panoramic images, point clouds, and positional information along the collection route from a ground-based perspective. Obtaining accurate matching results between point clouds and images is a key issue in data application from vehicle-borne MMSs. Traditional matching methods, such as point cloud projection, depth map generation, and point cloud coloring, are significantly affected by the processing methods of point clouds and matching logic. In this study, we propose a method for generating matching relationships based on panoramic images, utilizing the raw point cloud map, a series of trajectory points, and the corresponding panoramic images acquired using a vehicle-borne MMS as input data. Through a point-cloud-processing workflow, irrelevant points in the point cloud map are removed, and the point cloud scenes corresponding to the trajectory points are extracted. A collinear model based on spherical projection is employed during the matching process to project the point cloud scenes to the panoramic images. An algorithm for vectorial angle selection is also designed to address filtering out the occluded point cloud projections during the matching process, generating a series of matching results between point clouds and panoramic images corresponding to the trajectory points. Experimental verification indicates that the method generates matching results with an average pixel error of approximately 2.82 pixels, and an average positional error of approximately 4 cm, thus demonstrating efficient processing. This method is suitable for the data fusion of panoramic images and point clouds acquired using vehicle-borne MMSs in road scenes, provides support for various algorithms based on visual features, and has promising applications in fields such as navigation, positioning, surveying, and mapping. Full article
(This article belongs to the Special Issue 3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing)
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21 pages, 8476 KiB  
Article
Enhanced Strapdown Inertial Navigation System (SINS)/LiDAR Tightly Integrated Simultaneous Localization and Mapping (SLAM) for Urban Structural Feature Weaken Occasions in Vehicular Platform
by Xu Xu, Lianwu Guan, Yanbin Gao, Yufei Chen and Zhejun Liu
Remote Sens. 2024, 16(14), 2527; https://doi.org/10.3390/rs16142527 - 10 Jul 2024
Cited by 2 | Viewed by 3503
Abstract
LiDAR-based simultaneous localization and mapping (SLAM) offer robustness against illumination changes, but the inherent sparsity of LiDAR point clouds poses challenges for continuous tracking and navigation, especially in feature-deprived scenarios. This paper proposes a novel LiDAR/SINS tightly integrated SLAM algorithm designed to address [...] Read more.
LiDAR-based simultaneous localization and mapping (SLAM) offer robustness against illumination changes, but the inherent sparsity of LiDAR point clouds poses challenges for continuous tracking and navigation, especially in feature-deprived scenarios. This paper proposes a novel LiDAR/SINS tightly integrated SLAM algorithm designed to address the localization challenges in urban environments characterized in sparse structural features. Firstly, the method extracts edge points from the LiDAR point cloud using a traditional segmentation method and clusters them to form distinctive edge lines. Then, a rotation-invariant feature—line distance—is calculated based on the edge line properties that were inspired by the traditional tightly integrated navigation system. This line distance is utilized as the observation in a Kalman filter that is integrated into a tightly coupled LiDAR/SINS system. This system tracks the same edge lines across multiple frames for filtering and correction instead of tracking points or LiDAR odometry results. Meanwhile, for loop closure, the method modifies the common SCANCONTEXT algorithm by designating all bins that do not reach the maximum height as special loop keys, which reduce false matches. Finally, the experimental validation conducted in urban environments with sparse structural features demonstrated a 17% improvement in positioning accuracy when compared to the conventional point-based methods. Full article
(This article belongs to the Special Issue 3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing)
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19 pages, 7684 KiB  
Article
A Building Point Cloud Extraction Algorithm in Complex Scenes
by Zhonghua Su, Jing Peng, Dajian Feng, Shihua Li, Yi Yuan and Guiyun Zhou
Remote Sens. 2024, 16(11), 1934; https://doi.org/10.3390/rs16111934 - 28 May 2024
Cited by 1 | Viewed by 846
Abstract
Buildings are significant components of digital cities, and their precise extraction is essential for the three-dimensional modeling of cities. However, it is difficult to accurately extract building features effectively in complex scenes, especially where trees and buildings are tightly adhered. This paper proposes [...] Read more.
Buildings are significant components of digital cities, and their precise extraction is essential for the three-dimensional modeling of cities. However, it is difficult to accurately extract building features effectively in complex scenes, especially where trees and buildings are tightly adhered. This paper proposes a highly accurate building point cloud extraction method based solely on the geometric information of points in two stages. The coarsely extracted building point cloud in the first stage is iteratively refined with the help of mask polygons and the region growing algorithm in the second stage. To enhance accuracy, this paper combines the Alpha Shape algorithm with the neighborhood expansion method to generate mask polygons, which help fill in missing boundary points caused by the region growing algorithm. In addition, this paper performs mask extraction on the original points rather than non-ground points to solve the problem of incorrect identification of facade points near the ground using the cloth simulation filtering algorithm. The proposed method has shown excellent extraction accuracy on the Urban-LiDAR and Vaihingen datasets. Specifically, the proposed method outperforms the PointNet network by 20.73% in precision for roof extraction of the Vaihingen dataset and achieves comparable performance with the state-of-the-art HDL-JME-GGO network. Additionally, the proposed method demonstrated high accuracy in extracting building points, even in scenes where buildings were closely adjacent to trees. Full article
(This article belongs to the Special Issue 3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing)
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40 pages, 22727 KiB  
Article
Image-Aided LiDAR Extraction, Classification, and Characterization of Lane Markings from Mobile Mapping Data
by Yi-Ting Cheng, Young-Ha Shin, Sang-Yeop Shin, Yerassyl Koshan, Mona Hodaei, Darcy Bullock and Ayman Habib
Remote Sens. 2024, 16(10), 1668; https://doi.org/10.3390/rs16101668 - 8 May 2024
Cited by 1 | Viewed by 1268
Abstract
The documentation of roadway factors (such as roadway geometry, lane marking retroreflectivity/classification, and lane width) through the inventory of lane markings can reduce accidents and facilitate road safety analyses. Typically, lane marking inventory is established using either imagery or Light Detection and Ranging [...] Read more.
The documentation of roadway factors (such as roadway geometry, lane marking retroreflectivity/classification, and lane width) through the inventory of lane markings can reduce accidents and facilitate road safety analyses. Typically, lane marking inventory is established using either imagery or Light Detection and Ranging (LiDAR) data collected by mobile mapping systems (MMS). However, it is important to consider the strengths and weaknesses of both camera and LiDAR units when establishing lane marking inventory. Images may be susceptible to weather and lighting conditions, and lane marking might be obstructed by neighboring traffic. They also lack 3D and intensity information, although color information is available. On the other hand, LiDAR data are not affected by adverse weather and lighting conditions, and they have minimal occlusions. Moreover, LiDAR data provide 3D and intensity information. Considering the complementary characteristics of camera and LiDAR units, an image-aided LiDAR framework would be highly advantageous for lane marking inventory. In this context, an image-aided LiDAR framework means that the lane markings generated from one modality (i.e., either an image or LiDAR) are enhanced by those derived from the other one (i.e., either imagery or LiDAR). In addition, a reporting mechanism that can handle multi-modal datasets from different MMS sensors is necessary for the visualization of inventory results. This study proposes an image-aided LiDAR lane marking inventory framework that can handle up to five lanes per driving direction, as well as multiple imaging and LiDAR sensors onboard an MMS. The framework utilizes lane markings extracted from images to improve LiDAR-based extraction. Thereafter, intensity profiles and lane width estimates can be derived using the image-aided LiDAR lane markings. Finally, imagery/LiDAR data, intensity profiles, and lane width estimates can be visualized through a web portal that has been developed in this study. For the performance evaluation of the proposed framework, lane markings obtained through LiDAR-based, image-based, and image-aided LiDAR approaches are compared against manually established ones. The evaluation demonstrates that the proposed framework effectively compensates for the omission errors in the LiDAR-based extraction, as evidenced by an increase in the recall from 87.6% to 91.6%. Full article
(This article belongs to the Special Issue 3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing)
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23 pages, 18047 KiB  
Article
A Multi-Level Robust Positioning Method for Three-Dimensional Ground Penetrating Radar (3D GPR) Road Underground Imaging in Dense Urban Areas
by Ju Zhang, Qingwu Hu, Yemei Zhou, Pengcheng Zhao and Xuzhe Duan
Remote Sens. 2024, 16(9), 1559; https://doi.org/10.3390/rs16091559 - 27 Apr 2024
Cited by 1 | Viewed by 1410
Abstract
Three-Dimensional Ground Penetrating Radar (3D GPR) detects subsurface targets non-destructively, rapidly, and continuously. The complex environment around urban roads affects the positioning accuracy of 3D GPR. The positioning accuracy directly affects the data quality, as inaccurate positioning can lead to distortion and misalignment [...] Read more.
Three-Dimensional Ground Penetrating Radar (3D GPR) detects subsurface targets non-destructively, rapidly, and continuously. The complex environment around urban roads affects the positioning accuracy of 3D GPR. The positioning accuracy directly affects the data quality, as inaccurate positioning can lead to distortion and misalignment of 3D GPR data. This paper proposed a multi-level robust positioning method to improve the positioning accuracy of 3D GPR in dense urban areas in order to obtain more accurate underground data. In environments with good GNSS signals, fast and high-precision positioning can be achieved based on GNSS data using differential GNSS technology; in scenes with weak GNSS signals, high-precision positioning of subsurface data can be achieved by using GNSS and IMU as well as using GNSS/INS tightly coupled solution technology; in scenes with no GNSS signals, SLAM technology is used for positioning based on INS data and 3D point cloud data. In summary, this method ensures a positioning accuracy of 3D GPR better than 10 cm and high-quality 3D images of underground urban roads in any environment. This provides data support for urban road underground structure surveys and has broad application prospects in underground disease detection and prevention. Full article
(This article belongs to the Special Issue 3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing)
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29 pages, 18448 KiB  
Article
Urban Building Height Extraction from Gaofen-7 Stereo Satellite Images Enhanced by Contour Matching
by Yunfan Cui, Shuangming Zhao, Wanshou Jiang and Guorong Yu
Remote Sens. 2024, 16(9), 1556; https://doi.org/10.3390/rs16091556 - 27 Apr 2024
Cited by 2 | Viewed by 1150
Abstract
The traditional method for extracting the heights of urban buildings involves utilizing dense matching algorithms on stereo images to generate a digital surface model (DSM). However, for urban buildings, the disparity discontinuity issue that troubles the dense matching algorithm makes the elevations of [...] Read more.
The traditional method for extracting the heights of urban buildings involves utilizing dense matching algorithms on stereo images to generate a digital surface model (DSM). However, for urban buildings, the disparity discontinuity issue that troubles the dense matching algorithm makes the elevations of high-rise buildings and the surrounding areas inaccurate. The occlusion caused by trees in greenbelts makes it difficult to accurately extract the ground elevation around the building. To tackle these problems, a method for building height extraction from Gaofen-7 (GF-7) stereo images enhanced by contour matching is presented. Firstly, a contour matching algorithm was proposed to extract accurate building roof elevation from GF-7 images. Secondly, a ground filtering algorithm was employed on the DSM to generate a digital elevation model (DEM), and ground elevation can be extracted from this DEM. The difference between the rooftop elevation and the ground elevation represents the building height. The presented method was verified in Yingde, Guangzhou, Guangdong Province, and Xi’an, Shaanxi Province. The experimental results demonstrate that our proposed method outperforms existing methods in building height extraction concerning accuracy. Full article
(This article belongs to the Special Issue 3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing)
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28 pages, 18297 KiB  
Article
LVI-Fusion: A Robust Lidar-Visual-Inertial SLAM Scheme
by Zhenbin Liu, Zengke Li, Ao Liu, Kefan Shao, Qiang Guo and Chuanhao Wang
Remote Sens. 2024, 16(9), 1524; https://doi.org/10.3390/rs16091524 - 25 Apr 2024
Cited by 6 | Viewed by 1740
Abstract
With the development of simultaneous positioning and mapping technology in the field of automatic driving, the current simultaneous localization and mapping scheme is no longer limited to a single sensor and is developing in the direction of multi-sensor fusion to enhance the robustness [...] Read more.
With the development of simultaneous positioning and mapping technology in the field of automatic driving, the current simultaneous localization and mapping scheme is no longer limited to a single sensor and is developing in the direction of multi-sensor fusion to enhance the robustness and accuracy. In this study, a localization and mapping scheme named LVI-fusion based on multi-sensor fusion of camera, lidar and IMU is proposed. Different sensors have different data acquisition frequencies. To solve the problem of time inconsistency in heterogeneous sensor data tight coupling, the time alignment module is used to align the time stamp between the lidar, camera and IMU. The image segmentation algorithm is used to segment the dynamic target of the image and extract the static key points. At the same time, the optical flow tracking based on the static key points are carried out and a robust feature point depth recovery model is proposed to realize the robust estimation of feature point depth. Finally, lidar constraint factor, IMU pre-integral constraint factor and visual constraint factor together construct the error equation that is processed with a sliding window-based optimization module. Experimental results show that the proposed algorithm has competitive accuracy and robustness. Full article
(This article belongs to the Special Issue 3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing)
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18 pages, 20434 KiB  
Article
High-Precision Map Construction in Degraded Long Tunnel Environments of Urban Subways
by Cheng Li, Wenbo Pan, Xiwen Yuan, Wenyu Huang, Chao Yuan, Quandong Wang and Fuyuan Wang
Remote Sens. 2024, 16(5), 809; https://doi.org/10.3390/rs16050809 - 26 Feb 2024
Cited by 2 | Viewed by 1274
Abstract
In response to the demand for high-precision point cloud mapping of subway trains in long tunnel degradation scenarios in major urban cities, we propose a map construction method based on LiDAR and inertial measurement sensors. This method comprises a tightly coupled frontend odometry [...] Read more.
In response to the demand for high-precision point cloud mapping of subway trains in long tunnel degradation scenarios in major urban cities, we propose a map construction method based on LiDAR and inertial measurement sensors. This method comprises a tightly coupled frontend odometry system based on error Kalman filters and backend optimization using factor graphs. In the frontend odometry, inertial calculation results serve as predictions for the filter, and residuals between LiDAR points and local map plane point clouds are used for filter updates. The global pose graph is constructed based on inter-frame odometry and other constraint factors, followed by a smoothing optimization for map building. Multiple experiments in subway tunnel scenarios demonstrate that the proposed method achieves robust trajectory estimation in long tunnel scenes, where classical multi-sensor fusion methods fail due to sensor degradation. The proposed method achieves a trajectory consistency of 0.1 m in tunnel scenes, meeting the accuracy requirements for train arrival, parking, and interval operations. Additionally, in an industrial park scenario, the method is compared with ground truth provided by inertial navigation, showing an accumulated error of less than 0.2%, indicating high precision. Full article
(This article belongs to the Special Issue 3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing)
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15 pages, 11133 KiB  
Article
Quasi-Dense Matching for Oblique Stereo Images through Semantic Segmentation and Local Feature Enhancement
by Guobiao Yao, Jin Zhang, Fengqi Zhu, Jianya Gong, Fengxiang Jin, Qingqing Fu and Xiaofang Ren
Remote Sens. 2024, 16(4), 632; https://doi.org/10.3390/rs16040632 - 8 Feb 2024
Cited by 1 | Viewed by 1144
Abstract
This paper proposes a quasi-dense feature matching algorithm that combines image semantic segmentation and local feature enhancement networks to address the problem of the poor matching of image features because of complex distortions, considerable occlusions, and a lack of texture on large oblique [...] Read more.
This paper proposes a quasi-dense feature matching algorithm that combines image semantic segmentation and local feature enhancement networks to address the problem of the poor matching of image features because of complex distortions, considerable occlusions, and a lack of texture on large oblique stereo images. First, a small amount of typical complex scene data are used to train the VGG16-UNet, followed by completing the semantic segmentation of multiplanar scenes across large oblique images. Subsequently, the prediction results of the segmentation are subjected to local adaptive optimization to obtain high-precision semantic segmentation results for each planar scene. Afterward, the LoFTR (Local Feature Matching with Transformers) strategy is used for scene matching, enabling enhanced matching for regions with poor local texture in the corresponding planes. The proposed method was tested on low-altitude large baseline stereo images of complex scenes and compared with five classical matching methods. Results reveal that the proposed method exhibits considerable advantages in terms of the number of correct matches, correct rate of matches, matching accuracy, and spatial distribution of corresponding points. Moreover, it is well-suitable for quasi-dense matching tasks of large baseline stereo images in complex scenes with considerable viewpoint variations. Full article
(This article belongs to the Special Issue 3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing)
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20 pages, 9271 KiB  
Article
A Novel Automatic Registration Method for Array InSAR Point Clouds in Urban Scenes
by Chenghao Cui, Yuling Liu, Fubo Zhang, Minan Shi, Longyong Chen, Wenjie Li and Zhenhua Li
Remote Sens. 2024, 16(3), 601; https://doi.org/10.3390/rs16030601 - 5 Feb 2024
Cited by 1 | Viewed by 1236
Abstract
The array interferometric synthetic aperture radar (Array InSAR) system resolves shadow issues by employing two scans in opposite directions, facilitating the acquisition of a comprehensive three-dimensional representation of the observed scene. The point clouds obtained from the two scans need to be transformed [...] Read more.
The array interferometric synthetic aperture radar (Array InSAR) system resolves shadow issues by employing two scans in opposite directions, facilitating the acquisition of a comprehensive three-dimensional representation of the observed scene. The point clouds obtained from the two scans need to be transformed into the same coordinate system using registration techniques to create a more comprehensive visual representation. However, the two-point clouds lack corresponding points and exhibit distinct geometric distortions, thereby preventing direct registration. This paper analyzes the error characteristics of array InSAR point clouds and proposes a robust registration method for array InSAR point clouds in urban scenes. It represents the 3D information of the point clouds using images, with pixel positions corresponding to the azimuth and ground range directions. Pixel intensity denotes the average height of points within the pixel. The KAZE algorithm and enhanced matching approach are used to obtain the homonymous points of two images, subsequently determining the transformation relationship between them. Experimental results with actual data demonstrate that, for architectural elements within urban scenes, the relative angular differences of registered facades are below 0.5°. As for ground elements, the Root Mean Square Error (RMSE) after registration is less than 1.5 m, thus validating the superiority of the proposed method. Full article
(This article belongs to the Special Issue 3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing)
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23 pages, 12227 KiB  
Article
3D Reconstruction of Ancient Buildings Using UAV Images and Neural Radiation Field with Depth Supervision
by Yingwei Ge, Bingxuan Guo, Peishuai Zha, San Jiang, Ziyu Jiang and Demin Li
Remote Sens. 2024, 16(3), 473; https://doi.org/10.3390/rs16030473 - 25 Jan 2024
Cited by 6 | Viewed by 2716
Abstract
The 3D reconstruction of ancient buildings through inclined photogrammetry finds a wide range of applications in surveying, visualization and heritage conservation. Unlike indoor objects, reconstructing ancient buildings presents unique challenges, including the slow speed of 3D reconstruction using traditional methods, the complex textures [...] Read more.
The 3D reconstruction of ancient buildings through inclined photogrammetry finds a wide range of applications in surveying, visualization and heritage conservation. Unlike indoor objects, reconstructing ancient buildings presents unique challenges, including the slow speed of 3D reconstruction using traditional methods, the complex textures of ancient structures and geometric issues caused by repeated textures. Additionally, there is a hash conflict problem when rendering outdoor scenes using neural radiation fields. To address these challenges, this paper proposes a 3D reconstruction method based on depth-supervised neural radiation fields. To enhance the representation of the geometric neural network, the addition of a truncated signed distance function (TSDF) supplements the existing signed distance function (SDF). Furthermore, the neural network’s training is supervised using depth information, leading to improved geometric accuracy in the reconstruction model through depth data obtained from sparse point clouds. This study also introduces a progressive training strategy to mitigate hash conflicts, allowing the hash table to express important details more effectively while reducing feature overlap. The experimental results demonstrate that our method, under the same number of iterations, produces images with clearer structural details, resulting in an average 15% increase in the Peak Signal-to-Noise Ratio (PSNR) value and a 10% increase in the Structural Similarity Index Measure (SSIM) value. Moreover, our reconstruction model produces higher-quality surface models, enabling the fast and highly geometrically accurate 3D reconstruction of ancient buildings. Full article
(This article belongs to the Special Issue 3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing)
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18 pages, 4654 KiB  
Article
On-Site Stability Assessment of Rubble Mound Breakwaters Using Unmanned Aerial Vehicle-Based Photogrammetry and Random Sample Consensus
by Marcos Arza-García, José Alberto Gonçalves, Vladimiro Ferreira Pinto and Guillermo Bastos
Remote Sens. 2024, 16(2), 331; https://doi.org/10.3390/rs16020331 - 14 Jan 2024
Cited by 3 | Viewed by 1495
Abstract
Traditional methods for assessing the stability of rubble mound breakwaters (RMBs) often rely on 2.5D data, which may fall short in capturing intricate changes in the armor units, such as tilting and lateral shifts. Achieving a detailed analysis of RMB geometry typically requires [...] Read more.
Traditional methods for assessing the stability of rubble mound breakwaters (RMBs) often rely on 2.5D data, which may fall short in capturing intricate changes in the armor units, such as tilting and lateral shifts. Achieving a detailed analysis of RMB geometry typically requires fully 3D methods, but these often hinge on expensive acquisition technologies like terrestrial laser scanning (TLS) or airborne light detection and ranging (LiDAR). This article introduces an innovative approach to evaluate the structural stability of RMBs by integrating UAV-based photogrammetry and the random sample consensus (RANSAC) algorithm. The RANSAC algorithm proves to be an efficient and scalable tool for extracting primitives from point clouds (PCs), effectively addressing challenges presented by outliers and data noise in photogrammetric PCs. Photogrammetric PCs of the RMB, generated using Structure-from-Motion and MultiView Stereo (SfM-MVS) from both pre- and post-storm flights, were subjected to the RANSAC algorithm for plane extraction and segmentation. Subsequently, a spatial proximity criterion was employed to match cuboids between the two time periods. The methodology was validated on the detached breakwater of Cabedelo do Douro in Porto, Portugal, with a specific focus on potential rotations or tilting of Antifer cubes within the protective layer. The results, assessing the effects of the Leslie storm in 2018, demonstrate the potential of our approach in identifying and quantifying structural changes in RMBs. Full article
(This article belongs to the Special Issue 3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing)
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21 pages, 75822 KiB  
Article
Reliable Feature Matching for Spherical Images via Local Geometric Rectification and Learned Descriptor
by San Jiang, Junhuan Liu, Yaxin Li, Duojie Weng and Wu Chen
Remote Sens. 2023, 15(20), 4954; https://doi.org/10.3390/rs15204954 - 13 Oct 2023
Cited by 3 | Viewed by 1759
Abstract
Spherical images have the advantage of recording full scenes using only one camera exposure and have been becoming an important data source for 3D reconstruction. However, geometric distortions inevitably exist due to the spherical camera imaging model. Thus, this study proposes a reliable [...] Read more.
Spherical images have the advantage of recording full scenes using only one camera exposure and have been becoming an important data source for 3D reconstruction. However, geometric distortions inevitably exist due to the spherical camera imaging model. Thus, this study proposes a reliable feature matching algorithm for spherical images via the combination of local geometric rectification and CNN (convolutional neural network) learned descriptor. First, image patches around keypoints are reprojected to their corresponding tangent planes based on a spherical camera imaging model, which uses scale and orientation data from the keypoints to achieve both rotation and scale invariance. Second, feature descriptors are then calculated from the rectified image patches by using a pre-trained separate detector and descriptor learning network, which improves the discriminability by exploiting the high representation learning ability of the CNN. Finally, after classical feature matching with the ratio test and cross check, refined matches are obtained based on an essential matrix-based epipolar geometry constraint for outlier removal. By using three real spherical images and an incremental structure from motion (SfM) engine, the proposed algorithm is verified and compared in terms of feature matching and image orientation. The experiment results demonstrate that the geometric distortions can be efficiently reduced from rectified image patches, and the increased ratio of the match numbers ranges from 26.8% to 73.9%. For SfM-based spherical image orientation, the proposed algorithm provides reliable feature matches to achieve complete reconstruction with comparative accuracy. Full article
(This article belongs to the Special Issue 3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing)
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18 pages, 20257 KiB  
Technical Note
Fast Digital Orthophoto Generation: A Comparative Study of Explicit and Implicit Methods
by Jianlin Lv, Guang Jiang, Wei Ding and Zhihao Zhao
Remote Sens. 2024, 16(5), 786; https://doi.org/10.3390/rs16050786 - 24 Feb 2024
Cited by 1 | Viewed by 1626
Abstract
A digital orthophoto is an image with geometric accuracy and no distortion. It is acquired through a top view of the scene and finds widespread applications in map creation, planning, and related fields. This paper classifies the algorithms for digital orthophoto generation into [...] Read more.
A digital orthophoto is an image with geometric accuracy and no distortion. It is acquired through a top view of the scene and finds widespread applications in map creation, planning, and related fields. This paper classifies the algorithms for digital orthophoto generation into two groups: explicit methods and implicit methods. Explicit methods rely on traditional geometric methods, obtaining geometric structure presented with explicit parameters with Multi-View Stereo (MVS) theories, as seen in our proposed Top view constrained Dense Matching (TDM). Implicit methods rely on neural rendering, obtaining implicit neural representation of scenes through the training of neural networks, as exemplified by Neural Radiance Fields (NeRFs). Both of them obtain digital orthophotos via rendering from a top-view perspective. In addition, this paper conducts an in-depth comparative study between explicit and implicit methods. The experiments demonstrate that both algorithms meet the measurement accuracy requirements and exhibit a similar level of quality in terms of generated results. Importantly, the explicit method shows a significant advantage in terms of efficiency, with a time consumption reduction of two orders of magnitude under our latest Compute Unified Device Architecture (CUDA) version TDM algorithm. Although explicit and implicit methods differ significantly in their representation forms, they share commonalities in the implementation across algorithmic stages. These findings highlight the potential advantages of explicit methods in orthophoto generation while also providing beneficial references and practical guidance for fast digital orthophoto generation using implicit methods. Full article
(This article belongs to the Special Issue 3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing)
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