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Advances in 3D Reconstruction Based on Remote Sensing Imagery and Lidar Point Cloud

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

Deadline for manuscript submissions: 31 May 2026 | Viewed by 21098

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Special Issue Editors

Dr. Fuxun Liang

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Guest Editor

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Interests: point cloud processing; urban intelligence

Dr. Shuang Song

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Guest Editor

Department of Civil, Environmental and Geodetic Engineering, Translational Data Analytics Institute, The Ohio State University, Columbus, OH, USA
Interests: sensor fusion; 3D computer vision; photogrammetry

Dr. Bing Wang

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Guest Editor

Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Interests: three-dimensional computer vision; machine learning; intelligent spatial perception; visual and LiDAR SLAM

Special Issue Information

Dear Colleagues,

As a fundamental problem in remote sensing, 3D reconstruction has continued to attract the attention of researchers over recent decades. The ability to create detailed digital replicas of physical spaces is essential for various applications, ranging from urban planning, building construction, and forest management to virtual tourism. This has led to an increased demand for sophisticated 3D reconstruction techniques that can capture the intricacies of built and natural environments.

Over the past few years, the methodologies for 3D reconstruction have advanced rapidly, with significant strides in data acquisition, deep learning algorithms, and large-scale model development. In terms of data, satellite and UAV imagery continue to grow while laser scanning equipment becomes increasingly affordable and widely available, and simulated point cloud data are gradually increasing. The integration of multi-modal data sources, such as satellite imagery, aerial photography, and point cloud, has enabled the creation of more comprehensive and accurate 3D models. Implicit 3D reconstruction methods (e.g., neural radiance fields, NeRF) and 3D Gaussian splatting (3DGS) have shown great potential in creating high-quality reconstructions from sparse input views. Furthermore, the advent of large models has revolutionized the field, with models capable of cross-modal learning and depth recovery, leading to more robust and automated reconstruction processes.

As a forum for recent advances and developments in the research and applications of 3D reconstruction from remote sensing imagery and LiDAR point cloud, especially with a focus on deep learning algorithms, this issue calls for the latest findings and innovative work conducted on understanding and modeling natural and artificial scenes, including related data generation and fusion or annotation methods. Submissions can cover one or more of the following themes:

Advances in 3D reconstruction algorithms and techniques;
The integration of multi-modal data sources for enhanced 3D modeling;
Three-dimensional reconstruction of architecture, cultural heritage, and natural scenes;
Three-dimensional reconstruction of disaster management and emergency response;
Real-scene 3D reconstruction for geological, topographical, and urban analysis;
Contributions of 3D reconstruction to a low-altitude economy.

Dr. Fuxun Liang
Dr. Shuang Song
Dr. Bing Wang
Guest Editors

Manuscript Submission Information

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Keywords

three-dimensional reconstruction
remote sensing image
LiDAR point cloud
multi-modality fusion
semantic segmentation
deep learning
neural radiation field (NeRF)
3D Gaussian splatting (3DGS)
generative modeling
large models
ubiquitous point cloud interpretation
3D scene modeling
building reconstruction
indoor modeling
natural scene reconstruction
digital twins

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Published Papers (8 papers)

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Research

37 pages, 36191 KB

Open AccessArticle

A Density-Guided and Residual-Feedback Denoising Method for Building Height Estimation from ICESat-2/ATLAS Data

by Pingbo Hu, Yichen Wang, Hanqi Chen, Yanan Liu and Xiulin Liu

Remote Sens. 2026, 18(4), 540; https://doi.org/10.3390/rs18040540 - 8 Feb 2026

Viewed by 292

Abstract

Building height is a critical parameter for urban analysis, yet accurately estimating it from ICESat-2 photon-counting LiDAR data remains challenging due to pervasive noise photons and uneven noise distribution. To address the limitations of fixed-threshold denoising methods and improve adaptability across varying density conditions, this study proposes a dual-stage denoising framework for ICESat-2 ATL03 photon data. In the first stage, local photon densities are estimated within a reliable radius, log-transformed, and stratified into multiple levels. Adaptive thresholds are then applied at each level to suppress low-density noise while minimizing over-filtering in sparse regions. In the second stage, residual feedback-driven adaptive fitting strategy is applied along the ground track, where polynomial fitting was performed in sliding windows, with the window size dynamically adjusted based on residuals to refine local structures and eliminate outliers. The experiment was conducted in South Holland and Friesland, across 84 ICESat-2 tracks, where quantitative evaluations under varying day/night and beam conditions confirmed the effectiveness of the proposed framework. For denoising, the proposed method achieved high denoising accuracy, with F1-scores exceeding 0.97 in most cases, outperforming previous methods. Furthermore, building heights derived from footprint buffering and elevation differencing are validated against airborne LiDAR, yielding coefficient of determination (R²) values of 0.7235 and 0.9487 for the two regions, with root mean square error (RMSE) values of 1.5045 m and 1.8849 m, respectively. This study confirms the effectiveness and robustness of the proposed dual-stage framework, demonstrating its strong capability for both noise suppression in ICESat-2 ATL03 photon data and the subsequent accurate estimation of building heights. Full article

(This article belongs to the Special Issue Advances in 3D Reconstruction Based on Remote Sensing Imagery and Lidar Point Cloud)

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24 pages, 3748 KB

Open AccessArticle

Automated Recognition of Rock Mass Discontinuities on Vegetated High Slopes Using UAV Photogrammetry and an Improved Superpoint Transformer

by Peng Wan, Xianquan Han, Ruoming Zhai and Xiaoqing Gan

Remote Sens. 2026, 18(2), 357; https://doi.org/10.3390/rs18020357 - 21 Jan 2026

Viewed by 327

Abstract

Automated recognition of rock mass discontinuities in vegetated high-slope terrains remains a challenging task critical to geohazard assessment and slope stability analysis. This study presents an integrated framework combining close-range UAV photogrammetry with an Improved Superpoint Transformer (ISPT) for semantic segmentation and structural characterization. High-resolution UAV imagery was processed using an SfM–MVS photogrammetric workflow to generate dense point clouds, followed by a three-stage filtering workflow comprising cloth simulation filtering, volumetric density analysis, and VDVI-based vegetation discrimination. Feature augmentation using volumetric density and the Visible-Band Difference Vegetation Index (VDVI), together with connected-component segmentation, enhanced robustness under vegetation occlusion. Validation on four vegetated slopes in Buyun Mountain, China, achieved an overall classification accuracy of 89.5%, exceeding CANUPO (78.2%) and the baseline SPT (85.8%), with a 25-fold improvement in computational efficiency. In total, 4918 structural planes were extracted, and their orientations, dip angles, and trace lengths were automatically derived. The proposed ISPT-based framework provides an efficient and reliable approach for high-precision geotechnical characterization in complex, vegetation-covered rock mass environments. Full article

(This article belongs to the Special Issue Advances in 3D Reconstruction Based on Remote Sensing Imagery and Lidar Point Cloud)

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32 pages, 5853 KB

Open AccessArticle

A Large-Scale 3D Gaussian Reconstruction Method for Optimized Adaptive Density Control in Training Resource Scheduling

by Ke Yan, Hui Wang, Zhuxin Li, Yuting Wang, Shuo Li and Hongmei Yang

Remote Sens. 2025, 17(23), 3868; https://doi.org/10.3390/rs17233868 - 28 Nov 2025

Cited by 1 | Viewed by 2477

Abstract

In response to the challenges of low computational efficiency, insufficient detail restoration, and dependence on multiple GPUs in 3D Gaussian Splatting for large-scale UAV scene reconstruction, this study introduces an improved 3D Gaussian Splatting framework. It primarily targets two aspects: optimization of the partitioning strategy and enhancement of adaptive density control. Specifically, an adaptive partitioning strategy guided by scene complexity is designed to ensure more balanced computational workloads across spatial blocks. To preserve scene integrity, auxiliary point clouds are integrated during partition optimization. Furthermore, a pixel weight-scaling mechanism is employed to regulate the average gradient in adaptive density control, thereby mitigating excessive densification of Gaussians. This design accelerates the training process while maintaining high-fidelity rendering quality. Additionally, a task-scheduling algorithm based on frequency-domain analysis is incorporated to further improve computational resource utilization. Extensive experiments on multiple large-scale UAV datasets demonstrate that the proposed framework can be trained efficiently on a single RTX 3090 GPU, achieving more than a 50% reduction in average optimization time while maintaining PSNR, SSIM and LPIPS values that are comparable to or better than representative 3DGS-based methods; on the MatrixCity-S dataset (>6000 images), it attains the highest PSNR among 3DGS-based approaches and completes training on a single 24 GB GPU in less than 60% of the training time of DOGS. Nevertheless, the current framework still requires several hours of optimization for city-scale scenes and has so far only been evaluated on static UAV imagery with a fixed camera model, which may limit its applicability to dynamic scenes or heterogeneous sensor configurations. Full article

(This article belongs to the Special Issue Advances in 3D Reconstruction Based on Remote Sensing Imagery and Lidar Point Cloud)

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27 pages, 33395 KB

Open AccessArticle

Deep Line-Segment Detection-Driven Building Footprints Extraction from Backpack LiDAR Point Clouds for Urban Scene Reconstruction

by Jia Li, Rushi Lv, Qiuping Lan, Xinyi Shou, Hengyu Ruan, Jianjun Cao and Zikuan Li

Remote Sens. 2025, 17(22), 3730; https://doi.org/10.3390/rs17223730 - 17 Nov 2025

Cited by 1 | Viewed by 1272

Abstract

Accurate and reliable extraction of building footprints from LiDAR point clouds is a fundamental task in remote sensing and urban scene reconstruction. Building footprints serve as essential geospatial products that support GIS database updating, land-use monitoring, disaster management, and digital twin development. Traditional image-based methods enable large-scale mapping but suffer from 2D perspective limitations and radiometric distortions, while airborne or vehicle-borne LiDAR systems often face single-viewpoint constraints that lead to incomplete or fragmented footprints. Recently, backpack mobile laser scanning (MLS) has emerged as a flexible platform for capturing dense urban geometry at the pedestrian level. However, the high noise, point sparsity, and structural complexity of MLS data make reliable footprints delineation particularly challenging. To address these issues, this study proposes a Deep Line-Segment Detection–Driven Building Footprints Extraction Framework that integrates multi-layer accumulated occupancy mapping, deep geometric feature learning, and structure-aware regularization. The accumulated occupancy maps aggregate stable wall features from multiple height slices to enhance contour continuity and suppress random noise. A deep line-segment detector is then employed to extract robust geometric cues from noisy projections, achieving accurate edge localization and reduced false responses. Finally, a structural chain-based completion and redundancy filtering strategy repairs fragmented contours and removes spurious lines, ensuring coherent and topologically consistent footprints reconstruction. Extensive experiments conducted on two campus scenes containing 102 buildings demonstrate that the proposed method achieves superior performance with an average Precision of 95.7%, Recall of 92.2%, F1-score of 93.9%, and IoU of 88.6%, outperforming existing baseline approaches by 4.5–7.8% in F1-score. These results highlight the strong potential of backpack LiDAR point clouds, when combined with deep line-segment detection and structural reasoning, to complement traditional remote sensing imagery and provide a reliable pathway for large-scale urban scene reconstruction and geospatial interpretation. Full article

(This article belongs to the Special Issue Advances in 3D Reconstruction Based on Remote Sensing Imagery and Lidar Point Cloud)

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38 pages, 10032 KB

Open AccessArticle

Closed and Structural Optimization for 3D Line Segment Extraction in Building Point Clouds

by Ruoming Zhai, Xianquan Han, Peng Wan, Jianzhou Li, Yifeng He and Bangning Ding

Remote Sens. 2025, 17(18), 3234; https://doi.org/10.3390/rs17183234 - 18 Sep 2025

Cited by 1 | Viewed by 1262

Abstract

The extraction of architectural structural line features can simplify the 3D spatial representation of built environments, reduce the storage and processing burden of large-scale point clouds, and provide essential geometric primitives for downstream modeling tasks. However, existing 3D line extraction methods suffer from incomplete and fragmented contours, with missing or misaligned intersections. To overcome these limitations, this study proposes a patch-level framework for 3D line extraction and structural optimization from building point clouds. The proposed method first partitions point clouds into planar patches and establishes local image planes for each patch, enabling a structured 2D representation of unstructured 3D data. Then, graph-cut segmentation is proposed to extract compact boundary contours, which are vectorized into closed lines and back-projected into 3D space to form the initial line segments. To improve geometric consistency, regularized geometric constraints, including adjacency, collinearity, and orthogonality constraints, are further designed to merge homogeneous segments, refine topology, and strengthen structural outlines. Finally, we evaluated the approach on three indoor building environments and four outdoor scenes, and experimental results show that it reduces noise and redundancy while significantly improving the completeness, closure, and alignment of 3D line features in various complex architectural structures. Full article

(This article belongs to the Special Issue Advances in 3D Reconstruction Based on Remote Sensing Imagery and Lidar Point Cloud)

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22 pages, 6200 KB

Open AccessArticle

Research on 3D Reconstruction Methods for Incomplete Building Point Clouds Using Deep Learning and Geometric Primitives

by Ziqi Ding, Yuefeng Lu, Shiwei Shao, Yong Qin, Miao Lu, Zhenqi Song and Dengkuo Sun

Remote Sens. 2025, 17(3), 399; https://doi.org/10.3390/rs17030399 - 24 Jan 2025

Cited by 3 | Viewed by 3843

Abstract

Point cloud data, known for their accuracy and ease of acquisition, are commonly used for reconstructing level of detail 2 (LoD-2) building models. However, factors like object occlusion can cause incompleteness, negatively impacting the reconstruction process. To address this challenge, this paper proposes a method for reconstructing LoD-2 building models from incomplete point clouds. We design a generative adversarial network model that incorporates geometric constraints. The generator utilizes a multilayer perceptron with a curvature attention mechanism to extract multi-resolution features from the input data and then generates the missing portions of the point cloud through fully connected layers. The discriminator iteratively refines the generator’s predictions using a loss function that is combined with plane-aware Chamfer distance. For model reconstruction, the proposed method extracts a set of candidate polygons from the point cloud and computes weights for each candidate polygon based on a weighted energy term tailored to building characteristics. The most suitable planes are retained to construct the LoD-2 building model. The performance of this method is validated through extensive comparisons with existing state-of-the-art methods, showing a 10.9% reduction in the fitting error of the reconstructed models, and real-world data are tested to evaluate the effectiveness of the method. Full article

(This article belongs to the Special Issue Advances in 3D Reconstruction Based on Remote Sensing Imagery and Lidar Point Cloud)

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19 pages, 1575 KB

Open AccessArticle

FIFA3D: Flow-Guided Feature Aggregation for Temporal Three-Dimensional Object Detection

by Ruiqi Ma, Chunwei Wang, Chi Chen, Yihan Zeng, Bijun Li, Qin Zou, Qingqiu Huang, Xinge Zhu and Hang Xu

Remote Sens. 2025, 17(3), 380; https://doi.org/10.3390/rs17030380 - 23 Jan 2025

Viewed by 2770

Abstract

Detecting accurate 3D bounding boxes from LiDAR point clouds is crucial for autonomous driving. Recent studies have shown the superiority of the performance of multi-frame 3D detectors, yet eliminating the misalignment across frames and effectively aggregating spatiotemporal information are still challenging problems. In this paper, we present a novel flow-guided feature aggregation scheme for 3D object detection (FIFA3D) to align cross-frame information. FIFA3D first leverages optical flow with supervised signals to model the pixel-to-pixel correlations between sequential frames. Considering the sparse nature of bird’s-eye-view feature maps, an additional classification branch is adopted to provide explicit pixel-wise clues. Meanwhile, we utilize multi-scale feature maps and predict flow in a coarse-to-fine manner. With guidance from the estimated flow, historical features can be well aligned to the current situation, and a cascade fusion strategy is introduced to benefit the following detection. Extensive experiments show that FIFA3D surpasses the single-frame baseline with remarkable margins of

+ 10.8 %

mAPH and

+ 6.8 %

mAP on the Waymo and nuScenes validation datasets and performs well compared with state-of-the-art methods. Full article

(This article belongs to the Special Issue Advances in 3D Reconstruction Based on Remote Sensing Imagery and Lidar Point Cloud)

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28 pages, 21353 KB

Open AccessArticle

ThermalGS: Dynamic 3D Thermal Reconstruction with Gaussian Splatting

by Yuxiang Liu, Xi Chen, Shen Yan, Zeyu Cui, Huaxin Xiao, Yu Liu and Maojun Zhang

Remote Sens. 2025, 17(2), 335; https://doi.org/10.3390/rs17020335 - 19 Jan 2025

Cited by 6 | Viewed by 6588

Abstract

Thermal infrared (TIR) images capture temperature in a non-invasive manner, making them valuable for generating 3D models that reflect the spatial distribution of thermal properties within a scene. Current TIR image-based 3D reconstruction methods primarily focus on static conditions, which only capture the spatial distribution of thermal radiation but lack the ability to represent its temporal dynamics. The absence of dedicated datasets and effective methods for dynamic 3D representation are two key challenges that hinder progress in this field. To address these challenges, we propose a novel dynamic thermal 3D reconstruction method, named ThermalGS, based on 3D Gaussian Splatting (3DGS). ThermalGS employs a data-driven approach to directly learn both scene structure and dynamic thermal representation, using RGB and TIR images as input. The position, orientation, and scale of Gaussian primitives are guided by the RGB mesh. We introduce feature encoding and embedding networks to integrate semantic and temporal information into the Gaussian primitives, allowing them to capture dynamic thermal radiation characteristics. Moreover, we construct the Thermal Scene Day-and-Night (TSDN) dataset, which includes multi-view, high-resolution aerial RGB reference images and TIR images captured at five different times throughout the day and night, providing a benchmark for dynamic thermal 3D reconstruction tasks. Experimental results demonstrate that the proposed method achieves state-of-the-art performance on the TSDN dataset, with an average absolute temperature error of 1 °C and the ability to predict surface temperature variations over time. Full article

(This article belongs to the Special Issue Advances in 3D Reconstruction Based on Remote Sensing Imagery and Lidar Point Cloud)

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