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Remote Sensing
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3D Scene Perception and Reconstruction of Remote Sensing Imagery
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3D Scene Perception and Reconstruction of Remote Sensing Imagery

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

Deadline for manuscript submissions: 20 February 2027 | Viewed by 2885

Editors

Prof. Dr. Jun Xiao

E-Mail Website
Guest Editor
School of Artificial Intelligence, University of Chinese Academy of Sciences, No. 19 Yuquan Road, Shijingshan District, Beijing 100049, China
Interests: 3D reconstruction; remote sensing; computer vision; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Haiyong Jiang

E-Mail Website
Guest Editor
School of Artificial Intelligence, University of Chinese Academy of Sciences, No. 19 Yuquan Road, Shijingshan District, Beijing 100049, China
Interests: 3D modeling; 3D understanding; 3D deep learning
Dr. Lupeng Liu

E-Mail
Guest Editor
School of Artificial Intelligence, University of Chinese Academy of Sciences, No. 19 Yuquan Road, Shijingshan District, Beijing 100049, China
Interests: 3D point cloud; 3D reconstruction; registration; computer vision
Special Issues, Collections and Topics in MDPI journals
Dr. Zhengda Lu

E-Mail Website
Guest Editor
School of Artificial Intelligence, University of Chinese Academy of Sciences, No. 19 Yuquan Road, Shijingshan District, Beijing 100049, China
Interests: 3D reconstruction; 3D scene understanding; computer vision

Special Issue Information

Dear Colleagues,

Three-dimensional scene perception and reconstruction represent fundamental research challenges in modern remote sensing, with critical applications spanning geographical surveys, urban planning, infrastructure monitoring, and disaster management. These complex tasks typically require the integration and processing of multimodal data from diverse sources, including satellite imagery, aerial LiDAR, UAV photogrammetry, and multi-spectral sensors. However, achieving accurate 3D scene interpretation and reconstruction from such heterogeneous datasets presents several key technical challenges:

  • Multi-modal data fusion: This entails the effective integration of disparate data formats with varying spatial resolutions, temporal frequencies, and coordinate systems.
  • Robust structural reconstruction and perception: This involves the ability to handle noisy inputs, severe occlusions, and complex geometric distortions inherent in real-world remote sensing data and to recover inherent semantic structures.
  • Annotation dependency: This involves overcoming the heavy reliance on densely annotated training data for deep learning approaches. 

This Special Issue seeks to showcase cutting-edge innovations in both theoretical advances and practical applications of 3D scene perception and reconstruction for remote sensing. We particularly welcome contributions that

  • Constitute novel methodologies for cross-modal data fusion and alignment;
  • Outline robust algorithms for handling imperfect or incomplete remote sensing data;
  • Advance innovative learning paradigms that reduce annotation requirements;
  • Undertake semantic and structural reconstruction for large-scale outdoor scenes;
  • Benchmark datasets and evaluation frameworks;
  • Present transformative applications in real-world scenarios.

This is not an exclusive list, and we encourage submissions addressing these challenges across all domains of remote sensing, and we have particular interest in solutions that bridge the gap between theoretical development and practical implementation.

Prof. Dr. Jun Xiao
Dr. Haiyong Jiang
Dr. Lupeng Liu
Dr. Zhengda Lu
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • multi-modal fusion
  • structural reconstruction
  • weak supervision and semi-supervision
  • remote sensing
  • annotation efficiency

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Published Papers (3 papers)

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Research

31 pages, 8755 KB  
Article
A Sub-Scene-Based GNSS-Constrained Structure from Motion for Robust Long-Corridor UAV Image Reconstruction
by Wei Huang, San Jiang, Xiangxiang Huang, Hongyun Lv, Yaqin Li and Zhu Tao
Remote Sens. 2026, 18(14), 2321; https://doi.org/10.3390/rs18142321 - 10 Jul 2026
Viewed by 335
Abstract
In long-corridor Unmanned Aerial Vehicle (UAV) photogrammetry, weak imaging geometry can compromise camera parameter estimation and lead to systematic reconstruction deformation, commonly referred to as the bowl effect, in conventional structure-from-motion (SfM) pipelines. To address this problem, this paper proposes a sub-scene-based GNSS [...] Read more.
In long-corridor Unmanned Aerial Vehicle (UAV) photogrammetry, weak imaging geometry can compromise camera parameter estimation and lead to systematic reconstruction deformation, commonly referred to as the bowl effect, in conventional structure-from-motion (SfM) pipelines. To address this problem, this paper proposes a sub-scene-based GNSS (Global Navigation Satellite System) constrained SfM framework for robust long-corridor UAV photogrammetric reconstruction. First, camera parameter gradients derived from epipolar geometry are used to construct a gradient-consistency cost for identifying stable sub-scenes. Second, GNSS-constrained incremental structureless bundle adjustment (BA) is performed to recover reliable initial camera poses and absolute scale based on image triplets and GNSS/POS observations. Finally, GNSS-weighted BA and inequality-constrained GNSS fusion are introduced to refine camera parameters under a single ground control point (GCP). Experiments on four UAV corridor datasets demonstrate that the proposed method effectively suppresses the bowl effect and stabilizes camera parameter estimation. The proposed method achieves average planar, vertical, and three-dimensional accuracies of 0.040 m, 0.032 m, and 0.051 m, respectively, using only one control point. Compared with the standard Colmap pipeline, the runtime is reduced by approximately 52%. In addition, the proposed method achieves a lower average three-dimensional checkpoint RMSE (0.051 m) than MicMac (0.056 m), Pix4D (0.074 m), Agisoft Metashape (0.078 m) and ContextCapture (0.060 m). Full article
(This article belongs to the Special Issue 3D Scene Perception and Reconstruction of Remote Sensing Imagery)
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27 pages, 39233 KB  
Article
DLG-GS: Dynamic Lighting-Aware Real-Time 3D Gaussian Splatting for Weak-Texture Tunnel Scenes
by Jun Li, Shuo Wang, Ronghao Yang, Shuai Shi and Zhenlong Liu
Remote Sens. 2026, 18(11), 1705; https://doi.org/10.3390/rs18111705 - 25 May 2026
Cited by 1 | Viewed by 835
Abstract
Recent advances in 3D Gaussian splatting (3DGS) have enabled efficient image-based scene reconstruction, but existing methods that rely heavily on multi-view photometric consistency remain sensitive to dynamic illumination and weakly constrained regions. This issue is especially evident in tunnel scenes, where limited ambient [...] Read more.
Recent advances in 3D Gaussian splatting (3DGS) have enabled efficient image-based scene reconstruction, but existing methods that rely heavily on multi-view photometric consistency remain sensitive to dynamic illumination and weakly constrained regions. This issue is especially evident in tunnel scenes, where limited ambient light and localized active illumination cause strong appearance variation and shadowed regions that appear weakly textured in the captured images. As a result, existing methods often suffer from appearance inconsistency, floating artifacts, and unstable Gaussian distributions. To address these challenges, we present dynamic lighting-aware Gaussian splatting (DLG-GS), a real-time framework designed primarily for tunnel-oriented reconstruction under dynamic lighting. DLG-GS includes two complementary components: a dynamic lighting-adaptive appearance modeling strategy that reduces illumination-induced artifacts while preserving local texture details, and a voxel–depth joint constraint that uses monocular depth priors to regularize the spatial distribution of voxel anchors and neural Gaussians, thereby improving optimization stability and suppressing floating artifacts in shadow-induced weak-texture regions. By jointly optimizing appearance adaptation and depth-guided spatial regularization, DLG-GS improves reconstruction stability and rendering quality while maintaining real-time performance. Experiments on a self-collected tunnel dataset show clear improvements over selected baselines, and additional evaluations on public benchmarks indicate competitive performance beyond the target tunnel setting. Full article
(This article belongs to the Special Issue 3D Scene Perception and Reconstruction of Remote Sensing Imagery)
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45 pages, 21152 KB  
Article
A 3D Gaussian Splatting Method with Deterministic Structure-Sensitive Adaptive Density Control for UAV Orthophoto Generation
by Ke Yan, Hui Wang, Zhuxin Li, Yuting Wang, Shuo Li and Liyong Wang
Remote Sens. 2026, 18(9), 1400; https://doi.org/10.3390/rs18091400 - 1 May 2026
Cited by 1 | Viewed by 1044
Abstract
Unmanned Aerial Vehicle (UAV) orthophoto generation in complex environments remains challenging because weak textures, reflective surfaces, occlusions, and large scene extents can cause incomplete reconstruction, ghosting, and seam artifacts. Although 3D Gaussian Splatting (3DGS) offers an efficient explicit scene representation, its use in [...] Read more.
Unmanned Aerial Vehicle (UAV) orthophoto generation in complex environments remains challenging because weak textures, reflective surfaces, occlusions, and large scene extents can cause incomplete reconstruction, ghosting, and seam artifacts. Although 3D Gaussian Splatting (3DGS) offers an efficient explicit scene representation, its use in large-scale UAV orthophoto generation is limited by high memory consumption, unstable densification, and insufficient support for mapping-oriented orthographic rendering. This paper proposes a single-GPU 3DGS framework for UAV orthophoto generation by integrating adaptive spatial block partitioning, deterministic structure-sensitive adaptive density control, and core–buffer tiled orthographic rendering with weighted blending. The proposed framework decomposes large scenes into resource-bounded subregions, guides Gaussian densification using fixed multi-view neighborhoods and edge-enhanced dynamic consistency, and generates large-format orthophotos with reduced boundary and seam artifacts. Experiments on MatrixCity-S and multiple UAV photogrammetric datasets show that the method achieves competitive reconstruction quality and improved resource efficiency. On MatrixCity-S, it reaches 29.01 dB PSNR and 0.901 SSIM, while completing training in 1 h 49 min on a single NVIDIA RTX 3090 GPU. Compared with BlockGS, peak VRAM consumption is reduced by more than 38% across datasets. Under geo-aligned comparison conditions, line-measurement comparisons with MetaShape and Pix4DMapper yield RMSE values of 0.099 m and 0.087 m, respectively. These results demonstrate the potential of the proposed framework for memory-efficient 3DGS-based UAV orthophoto generation under constrained hardware resources, while further control-point-based validation is still needed for rigorous surveying-grade applications. Full article
(This article belongs to the Special Issue 3D Scene Perception and Reconstruction of Remote Sensing Imagery)
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