Elevation Estimation-Driven Building 3-D Reconstruction From Single-View Remote Sensing Imagery

Mao, Yiqi; Chen, Kaiqiang; Zhao, Liangjin; Chen, Wei; Tang, Deke; Liu, Wenjie; Wang, Zhirui; Diao, Wenhui; Sun, Xian; Fu, Kun

doi:10.1109/tgrs.2023.3266477

Cited by 9 publications

(5 citation statements)

References 56 publications

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“…For the evaluation utilizing Vaihingen dataset, our proposed method, HeightFormer, was juxtaposed with existing models such as D3Net [38], Amirkolaee et al [53], PSDNet [54], Li et al [37], WMD [55], LeReS [56], ASSEH [57], Depthformer [52], Binsformer [51], and SFFDE [58] at Table 1. Rel and RMSE(log) primarily quantify the average error in pixel prediction.…”

Section: Quantitative and Qualitative Analysis On Vaihingenmentioning

confidence: 99%

HeightFormer: A Multilevel Interaction and Image-Adaptive Classification–Regression Network for Monocular Height Estimation with Aerial Images

Chen,

Zhang,

et al. 2024

Remote Sensing

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Height estimation has long been a pivotal topic within measurement and remote sensing disciplines, with monocular height estimation offering wide-ranging data sources and convenient deployment. This paper addresses the existing challenges in monocular height estimation methods, namely the difficulty in simultaneously achieving high-quality instance-level height and edge reconstruction, along with high computational complexity. This paper presents a comprehensive solution for monocular height estimation in remote sensing, termed HeightFormer, combining multilevel interactions and image-adaptive classification–regression. It features the Multilevel Interaction Backbone (MIB) and Image-adaptive Classification–regression Height Generator (ICG). MIB supplements the fixed sample grid in the CNN of the conventional backbone network with tokens of different interaction ranges. It is complemented by a pixel-, patch-, and feature map-level hierarchical interaction mechanism, designed to relay spatial geometry information across different scales and introducing a global receptive field to enhance the quality of instance-level height estimation. The ICG dynamically generates height partition for each image and reframes the traditional regression task, using a refinement from coarse to fine classification–regression that significantly mitigates the innate ill-posedness issue and drastically improves edge sharpness. Finally, the study conducts experimental validations on the Vaihingen and Potsdam datasets, with results demonstrating that our proposed method surpasses existing techniques.

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Section: Quantitative and Qualitative Analysis On Vaihingenmentioning

confidence: 99%

HeightFormer: A Multilevel Interaction and Image-Adaptive Classification–Regression Network for Monocular Height Estimation with Aerial Images

Chen,

Zhang,

et al. 2024

Remote Sensing

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“…Another approach is separating extracted features into different groups to reduce the interference of vegetation change in target detection [52]. In this regard, the application of Semantic Flow Field-guided DSM Estimation (SFFDE) can generate an elevation map (with vegetation) on the one hand, and on the other hand, filter vegetation out to generate a building mask [53].…”

Section: Related Work Of Gan Vegetation Segmentation and Sfmmentioning

confidence: 99%

Enhancing Building Point Cloud Reconstruction from RGB UAV Data with Machine-Learning-Based Image Translation

Dippold,

Tsai

2024

Sensors

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The performance of three-dimensional (3D) point cloud reconstruction is affected by dynamic features such as vegetation. Vegetation can be detected by near-infrared (NIR)-based indices; however, the sensors providing multispectral data are resource intensive. To address this issue, this study proposes a two-stage framework to firstly improve the performance of the 3D point cloud generation of buildings with a two-view SfM algorithm, and secondly, reduce noise caused by vegetation. The proposed framework can also overcome the lack of near-infrared data when identifying vegetation areas for reducing interferences in the SfM process. The first stage includes cross-sensor training, model selection and the evaluation of image-to-image RGB to color infrared (CIR) translation with Generative Adversarial Networks (GANs). The second stage includes feature detection with multiple feature detector operators, feature removal with respect to the NDVI-based vegetation classification, masking, matching, pose estimation and triangulation to generate sparse 3D point clouds. The materials utilized in both stages are a publicly available RGB-NIR dataset, and satellite and UAV imagery. The experimental results indicate that the cross-sensor and category-wise validation achieves an accuracy of 0.9466 and 0.9024, with a kappa coefficient of 0.8932 and 0.9110, respectively. The histogram-based evaluation demonstrates that the predicted NIR band is consistent with the original NIR data of the satellite test dataset. Finally, the test on the UAV RGB and artificially generated NIR with a segmentation-driven two-view SfM proves that the proposed framework can effectively translate RGB to CIR for NDVI calculation. Further, the artificially generated NDVI is able to segment and classify vegetation. As a result, the generated point cloud is less noisy, and the 3D model is enhanced.

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“…Partovi et al [13] used methods combining DSMs with multispectral satellite orthophotos to achieve the extraction, decomposition, and connection of building boundaries, culminating in the reconstruction of three-dimensional building models. Mao et al [14] employed deep-learning techniques to predict potential DSMs from orthophotos, aiming to reconstruct three-dimensional building models using the input orthophotos. Gui et al [8] adopted a model-driven strategy to extract features from DSMs and ultra-high-resolution satellite orthophotos for the reconstruction of building models.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep-Learning-Based Automated Building Information Modeling Reconstruction Using Orthophotos with Digital Surface Models

Wang,

Jiang,

Liu

2024

Buildings

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In the field of building information modeling (BIM), converting existing buildings into BIM by using orthophotos with digital surface models (DSMs) is a critical technical challenge. Currently, the BIM reconstruction process is hampered by the inadequate accuracy of building boundary extraction when carried out using existing technology, leading to insufficient correctness in the final BIM reconstruction. To address this issue, this study proposes a novel deep-learning- and postprocessing-based approach to automating reconstruction in BIM by using orthophotos with DSMs. This approach aims to improve the efficiency and correctness of the reconstruction of existing buildings in BIM. The experimental results in the publicly available Tianjin and Urban 3D reconstruction datasets showed that this method was able to extract accurate and regularized building boundaries, and the correctness of the reconstructed BIM was 85.61% and 82.93%, respectively. This study improved the technique of extracting regularized building boundaries from orthophotos and DSMs and achieved significant results in enhancing the correctness of BIM reconstruction. These improvements are helpful for the reconstruction of existing buildings in BIM, and this study provides a solid foundation for future improvements to the algorithm.

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Elevation Estimation-Driven Building 3-D Reconstruction From Single-View Remote Sensing Imagery

Cited by 9 publications

References 56 publications

HeightFormer: A Multilevel Interaction and Image-Adaptive Classification–Regression Network for Monocular Height Estimation with Aerial Images

HeightFormer: A Multilevel Interaction and Image-Adaptive Classification–Regression Network for Monocular Height Estimation with Aerial Images

Enhancing Building Point Cloud Reconstruction from RGB UAV Data with Machine-Learning-Based Image Translation

Deep-Learning-Based Automated Building Information Modeling Reconstruction Using Orthophotos with Digital Surface Models

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