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LiDAR and Point Cloud Processing for Digital Surface Modelling and 3D Scene Reconstruction

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

Deadline for manuscript submissions: closed (15 June 2024) | Viewed by 22311

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Special Issue Editors

Dr. Hadi AliAkbarpour

E-Mail Website
Guest Editor

Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
Interests: computer vision; computational geometry; 3D reconstruction and point cloud analysis; multi-view image analysis; machine learning/deep learning related to computer vision and remote sensing

Dr. Yuan Li

E-Mail Website
Guest Editor

School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai 519082, China
Interests: geoinformation; 3D modelling; photogrammetry; 3D point cloud
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last few decades, the rapid development of light detection and ranging (LiDAR) and multi-view stereo (MVS) has led to point cloud becoming an essential data source for a diverse array of purposes, one of which is to produce digital terrain models and 3D city models. Point cloud provides straightforward 3D spatial information, with extra information such as reflection intensity, echoes, color, etc.; meanwhile, point cloud is massive, unorganized, and unordered, and such characteristics bring challenges to the conversion of point cloud to structural and informative geographic models. Special attention has been paid to this specific field using data from different sources and platforms (e.g., terrestrial, vehicle-borne, and air-borne) with a wide range of technologies. However, the geographical complexity of some particular landforms and the fast change in urban environment still require more effective and efficient solutions for digital surface modeling and 3D scene reconstruction.

This Special Issue will highlight the studies and applications of point cloud in geographic mapping and modeling. Specifically, recent advances in deep learning methods, the integration of multisource and multiplatform data, the semantic and topographic interpretation of scenes, and the generation of standard-format models (e.g., CityGML) are covered in this Special Issue.

Articles may address but are not limited to the following topics:

Terrain filtering;
Point cloud segmentation and classification;
Integration/registration of multiplatform point clouds;
Fusion of point cloud with spectral data;
Deep learning of point cloud;
Object (e.g., building and road) extraction from point cloud;
3D urban reconstruction from point cloud;
Point cloud modeling in other applications (e.g., forest inventory, electrical utility, and historic preservation).

Dr. Hadi AliAkbarpour
Dr. Yuan Li
Guest Editors

Manuscript Submission Information

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Keywords

LiDAR
point cloud
geographic mapping
semantic segmentation
3D reconstruction
object extraction
data integration
ground filtering

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Published Papers (9 papers)

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Research

26 pages, 14305 KiB

Open AccessArticle

Evaluation of Traffic Sign Occlusion Rate Based on a 3D Point Cloud Space

by Ziqi Wang, Xiaofei Wang, Jun Li and Jiangbei Yao

Remote Sens. 2024, 16(16), 2872; https://doi.org/10.3390/rs16162872 - 6 Aug 2024

Cited by 1 | Viewed by 1779

Abstract

The effectiveness of road signs is hindered by obstructions, such as vegetation, mutual obstruction of signs, or the road alignment itself. The traditional evaluation of road sign recognition effectiveness is conducted through in-vehicle field surveys. However, this method has several drawbacks, including discontinuous identification, unclear positioning, incomplete coverage, and being time-consuming. Consequently, it is unable to effectively assess the recognition status of road signs at any arbitrary point within the road space. Therefore, this study employed laser scanning to construct a point-surface model, which was based on a point cloud algorithm and SLAM (Simultaneous Localization and Mapping), integrated LiDAR and inertial navigation system data, and optimized the point model after processing steps such as denoising, resampling, and three-dimensional model construction. Furthermore, a method for assessing the highway sign occlusion rate based on the picking algorithm was proposed. The algorithm was applied to an actual road environment, and the occlusion by other items was simulated. The results demonstrated the effectiveness of the method. This new method provides support for the fast and accurate calculation of road sign occlusion rates, which is of great importance for ensuring the safe traveling of vehicles. Full article

(This article belongs to the Special Issue LiDAR and Point Cloud Processing for Digital Surface Modelling and 3D Scene Reconstruction)

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20 pages, 39702 KiB

Open AccessArticle

Spatial Information Enhancement with Multi-Scale Feature Aggregation for Long-Range Object and Small Reflective Area Object Detection from Point Cloud

by Hanwen Li, Huamin Tao, Qiuqun Deng, Shanzhu Xiao and Jianxiong Zhou

Remote Sens. 2024, 16(14), 2631; https://doi.org/10.3390/rs16142631 - 18 Jul 2024

Viewed by 921

Abstract

Accurate and comprehensive 3D objects detection is important for perception systems in autonomous driving. Nevertheless, contemporary mainstream methods tend to perform more effectively on large objects in regions proximate to the LiDAR, leaving limited exploration of long-range objects and small objects. The divergent point pattern of LiDAR, which results in a reduction in point density as the distance increases, leads to a non-uniform point distribution that is ill-suited to discretized volumetric feature extraction. To address this challenge, we propose the Foreground Voxel Proposal (FVP) module, which effectively locates and generates voxels at the foreground of objects. The outputs are subsequently merged to mitigating the difference in point cloud density and completing the object shape. Furthermore, the susceptibility of small objects to occlusion results in the loss of feature space. To overcome this, we propose the Multi-Scale Feature Integration Network (MsFIN), which captures contextual information at different ranges. Subsequently, the outputs of these features are integrated through a cascade framework based on transformers in order to supplement the object features space. The extensive experimental results demonstrate that our network achieves remarkable results. Remarkably, our approach demonstrated an improvement of 8.56% AP on the SECOND baseline for the Car detection task at a distance of more than 20 m, and 9.38% AP on the Cyclist detection task. Full article

(This article belongs to the Special Issue LiDAR and Point Cloud Processing for Digital Surface Modelling and 3D Scene Reconstruction)

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22 pages, 2739 KiB

Open AccessArticle

A Registration Method of Overlap Aware Point Clouds Based on Transformer-to-Transformer Regression

by Yafei Zhao, Lineng Chen, Quanchen Zhou, Jiabao Zuo, Huan Wang and Mingwu Ren

Remote Sens. 2024, 16(11), 1898; https://doi.org/10.3390/rs16111898 - 25 May 2024

Cited by 1 | Viewed by 1682

Abstract

Transformer has recently become widely adopted in point cloud registration. Nevertheless, Transformer is unsuitable for handling dense point clouds due to resource constraints and the sheer volume of data. We propose a method for directly regressing the rigid relative transformation of dense point cloud pairs. Specifically, we divide the dense point clouds into blocks according to the down-sampled superpoints. During training, we randomly select point cloud blocks with varying overlap ratios, and during testing, we introduce the overlap-aware Rotation-Invariant Geometric Transformer Cross-Encoder (RIG-Transformer), which predicts superpoints situated within the common area of the point cloud pairs. The dense points corresponding to the superpoints are inputted into the Transformer Cross-Encoder to estimate their correspondences. Through the fusion of our RIG-Transformer and Transformer Cross-Encoder, we propose Transformer-to-Transformer Regression (TTReg), which leverages dense point clouds from overlapping regions for both training and testing phases, calculating the relative transformation of the dense points by using the predicted correspondences without random sample consensus (RANSAC). We have evaluated our method on challenging benchmark datasets, including 3DMatch, 3DLoMatch, ModelNet, and ModelLoNet, demonstrating up to a

7.2 %

improvement in registration recall. The improvements are attributed to our RIG-Transformer module and regression mechanism, which makes the features of superpoints more discriminative. Full article

(This article belongs to the Special Issue LiDAR and Point Cloud Processing for Digital Surface Modelling and 3D Scene Reconstruction)

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21 pages, 11620 KiB

Open AccessArticle

A Real-Time Method for Railway Track Detection and 3D Fitting Based on Camera and LiDAR Fusion Sensing

by Tiejian Tang, Jinghao Cao, Xiong Yang, Sheng Liu, Dongsheng Zhu, Sidan Du and Yang Li

Remote Sens. 2024, 16(8), 1441; https://doi.org/10.3390/rs16081441 - 18 Apr 2024

Cited by 2 | Viewed by 3838

Abstract

Railway track detection, which is crucial for train operational safety, faces numerous challenges such as the curved track, obstacle occlusion, and vibrations during the train’s operation. Most existing methods for railway track detection use a camera or LiDAR. However, the vision-based approach lacks essential 3D environmental information about the train, while the LiDAR-based approach tends to detect tracks of insufficient length due to the inherent limitations of LiDAR. In this study, we propose a real-time method for railway track detection and 3D fitting based on camera and LiDAR fusion sensing. Semantic segmentation of the railway track in the image is performed, followed by inverse projection to obtain 3D information of the distant railway track. Then, 3D fitting is applied to the inverse projection of the railway track for track vectorization and LiDAR railway track point segmentation. The extrinsic parameters necessary for inverse projection are continuously optimized to ensure robustness against variations in extrinsic parameters during the train’s operation. Experimental results show that the proposed method achieves desirable accuracy for railway track detection and 3D fitting with acceptable computational efficiency, and outperforms existing approaches based on LiDAR, camera, and camera–LiDAR fusion. To the best of our knowledge, our approach represents the first successful attempt to fuse camera and LiDAR data for real-time railway track detection and 3D fitting. Full article

(This article belongs to the Special Issue LiDAR and Point Cloud Processing for Digital Surface Modelling and 3D Scene Reconstruction)

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21 pages, 9997 KiB

Open AccessArticle

IPCONV: Convolution with Multiple Different Kernels for Point Cloud Semantic Segmentation

by Ruixiang Zhang, Siyang Chen, Xuying Wang and Yunsheng Zhang

Remote Sens. 2023, 15(21), 5136; https://doi.org/10.3390/rs15215136 - 27 Oct 2023

Cited by 5 | Viewed by 2288

Abstract

The segmentation of airborne laser scanning (ALS) point clouds remains a challenge in remote sensing and photogrammetry. Deep learning methods, such as KPCONV, have proven effective on various datasets. However, the rigid convolutional kernel strategy of KPCONV limits its potential use for 3D object segmentation due to its uniform approach. To address this issue, we propose an Integrated Point Convolution (IPCONV) based on KPCONV, which utilizes two different convolution kernel point generation strategies, one cylindrical and one a spherical cone, for more efficient learning of point cloud data features. We propose a customizable Multi-Shape Neighborhood System (MSNS) to balance the relationship between these convolution kernel point generations. Experiments on the ISPRS benchmark dataset, LASDU dataset, and DFC2019 dataset demonstrate the validity of our method. Full article

(This article belongs to the Special Issue LiDAR and Point Cloud Processing for Digital Surface Modelling and 3D Scene Reconstruction)

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21 pages, 2760 KiB

Open AccessArticle

Robust Semi-Supervised Point Cloud Registration via Latent GMM-Based Correspondence

by Zhengyan Zhang, Erli Lyu, Zhe Min, Ang Zhang, Yue Yu and Max Q.-H. Meng

Remote Sens. 2023, 15(18), 4493; https://doi.org/10.3390/rs15184493 - 12 Sep 2023

Cited by 5 | Viewed by 2259

Abstract

Due to the fact that point clouds are always corrupted by significant noise and large transformations, aligning two point clouds by deep neural networks is still challenging. This paper presents a semi-supervised point cloud registration (PCR) method for accurately estimating point correspondences and handling large transformations using limited prior datasets. Firstly, a modified autoencoder is introduced as the feature extraction module to extract the distinctive and robust features for the downstream registration task. Unlike optimization-based pairwise PCR strategies, the proposed method treats two point clouds as two implementations of a Gaussian mixture model (GMM), which we call latent GMM. Based on the above assumption, two point clouds can be regarded as two probability distributions. Hence, the PCR of two point clouds can be approached by minimizing the KL divergence between these two probability distributions. Then, the correspondence between the point clouds and the latent GMM components is estimated using the augmented regression network. Finally, the parameters of GMM can be updated by the correspondence and the transformation matrix can be computed by employing the weighted singular value decomposition (SVD) method. Extensive experiments conducted on both synthetic and real-world data validate the superior performance of the proposed method compared to state-of-the-art registration methods. These experiments also highlight the method’s superiority in terms of accuracy, robustness, and generalization. Full article

(This article belongs to the Special Issue LiDAR and Point Cloud Processing for Digital Surface Modelling and 3D Scene Reconstruction)

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23 pages, 13071 KiB

Open AccessArticle

An Object-Based Ground Filtering of Airborne LiDAR Data for Large-Area DTM Generation

by Hunsoo Song and Jinha Jung

Remote Sens. 2023, 15(16), 4105; https://doi.org/10.3390/rs15164105 - 21 Aug 2023

Cited by 8 | Viewed by 2439

Abstract

Digital terrain model (DTM) creation is a modeling process that represents the Earth’s surface. An aptly designed DTM generation method tailored for intended study can significantly streamline ensuing processes and assist in managing errors and uncertainties, particularly in large-area projects. However, existing methods often exhibit inconsistent and inexplicable results, struggle to clearly define what an object is, and often fail to filter large objects due to their locally confined operations. We introduce a new DTM generation method that performs object-based ground filtering, which is particularly beneficial for urban topography. This method defines objects as areas fully enclosed by steep slopes and grounds as smoothly connected areas, enabling reliable “object-based” segmentation and filtering, extending beyond the local context. Our primary operation, controlled by a slope threshold parameter, simplifies tuning and ensures predictable results, thereby reducing uncertainties in large-area modeling. Uniquely, our method considers surface water bodies in modeling and treats connected artificial terrains (e.g., overpasses) as ground. This contrasts with conventional methods, which often create noise near water bodies and behave inconsistently around overpasses and bridges, making our approach particularly beneficial for large-area 3D urban mapping. Examined on extensive and diverse datasets, our method offers unique features and high accuracy, and we have thoroughly assessed potential artifacts to guide potential users. Full article

(This article belongs to the Special Issue LiDAR and Point Cloud Processing for Digital Surface Modelling and 3D Scene Reconstruction)

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19 pages, 13653 KiB

Open AccessArticle

Semi-Automated BIM Reconstruction of Full-Scale Space Frames with Spherical and Cylindrical Components Based on Terrestrial Laser Scanning

by Guozhong Cheng, Jiepeng Liu, Dongsheng Li and Y. Frank Chen

Remote Sens. 2023, 15(11), 2806; https://doi.org/10.3390/rs15112806 - 28 May 2023

Cited by 4 | Viewed by 2639

Abstract

As-built building information modeling (BIM) model has gained more attention due to its increasing applications in construction, operation, and maintenance. Although methods for generating the as-built BIM model from laser scanning data have been proposed, few studies were focused on full-scale structures. To address this issue, this study proposes a semi-automated and effective approach to generate the as-built BIM model for a full-scale space frame structure with terrestrial laser scanning data, including the large-scale point cloud data (PCD) registration, large-scale PCD segmentation, and geometric parameters estimation. In particular, an effective coarse-to-fine data registration method was developed based on sphere targets and the oriented bounding box. Then, a novel method for extracting the sphere targets from full-scale structures was proposed based on the voxelization algorithm and random sample consensus (RANSAC) algorithm. Next, an efficient method for extracting cylindrical components was presented based on the detected sphere targets. The proposed approach is shown to be effective and reliable through the application of actual space frame structures. Full article

(This article belongs to the Special Issue LiDAR and Point Cloud Processing for Digital Surface Modelling and 3D Scene Reconstruction)

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20 pages, 14779 KiB

Open AccessArticle

High-Resolution and Efficient Neural Dual Contouring for Surface Reconstruction from Point Clouds

by Qi Liu, Jun Xiao, Lupeng Liu, Yunbiao Wang and Ying Wang

Remote Sens. 2023, 15(9), 2267; https://doi.org/10.3390/rs15092267 - 25 Apr 2023

Cited by 2 | Viewed by 2674

Abstract

The surface mesh reconstruction from point clouds has been a fundamental research topic in Computer Vision and Computer Graphics. Recently, the Neural Implicit Representation (NIR)-based reconstruction has revolutionized this research topic. This work summarizes and analyzes representative works on NIR-based reconstruction and highlights several important insights. However, one major problem with existing works is that they struggle to handle high-resolution meshes. To address this, this paper introduces HRE-NDC, a novel High-Resolution and Efficient Neural Dual Contouring approach for mesh reconstruction from point clouds, which takes the previous state-of-the-art as a baseline and adopts a coarse-to-fine network structure design, along with feature-preserving downsampling and other improvements. HRE-NDC significantly reduces training time and memory usage while achieving better surface reconstruction results. Experimental results demonstrate the superiority of our method in both visualization and quantitative results, and it shows excellent generalization performance on various data including large indoor scenes, real-scanned urban buildings, clothing, and human bodies. Full article

(This article belongs to the Special Issue LiDAR and Point Cloud Processing for Digital Surface Modelling and 3D Scene Reconstruction)

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