Generation of concise 3D building model from dense meshes by extracting and completing planar primitives

The widely used unmanned aerial vehicle oblique photogrammetry often suffers from information loss in complex urban environments, leading to geometric and textural defects in the resulting models. In this study, a close‐range panoramic optimal viewpoint selection assisted 3D urban scene reconstruction enhancement method is proposed for areas prone to defects. We first introduce the ground panoramic data acquisition equipment and strategy, which are different from those of the single‐lens supplementary photography method. Data acquisition is accomplished through a single and continuous surround‐style collection approach. The full coverage of the panoramic video in the space–time dimension enables the acquisition of texture details without considering camera station planning. Then, a panoramic multiview image generation approach is proposed. Adaptive viewpoint selection is achieved using unbiased sampling points from the rough scene model, and viewpoint optimisation is adopted to ensure sufficient image overlap and intersection effects, thus improving the scene reconstructability. Finally, the 3D model is generated by photogrammetric processing of the panoramic multiview images, resulting in an enhanced modelling effect. To validate the proposed method, we conducted experiments using real data from Qingdao, China. Both the qualitative and quantitative results demonstrate a significant improvement in the quality of geometric and textural reconstruction. The tie‐point reprojection errors are less than 1 pixel, and the registration accuracy with the model from oblique photogrammetry is comparable to that of optimised‐view photography. By eliminating the need for on‐site camera station planning or manual flight operations and effectively minimising the redundancy of panoramic videos, our approach significantly reduces the photography and computation costs associated with reconstruction enhancement. Thus, it presents a feasible technical solution for the generation of urban 3D fine models.

Section: Introductionmentioning

confidence: 99%

A 3D urban scene reconstruction enhancement approach based on adaptive viewpoint selection of panoramic videos

Zhang,

Hu,

et al. 2024

“…The Digital Surface Model (DSM), as one of the most fundamental geographical products, has been widely used in various applications, such as 3D city modelling, 3D land‐use management and disaster monitoring (Gruen et al., 2013; Han et al., 2020; Liu et al., 2023; Lv et al., 2022; Lv, Zhong, Wang, You, & Falco, 2023; Lv, Zhong, Wang, You, & Shi, 2023; Zhao et al., 2022). Satellite stereos, due to their characteristics of flexible acquisition and low cost, have been the dominant data source for generating city‐ or country‐level DSMs (Bosch et al., 2016; Gao et al., 2021; Huang et al., 2016; Kendall et al., 2017; Leotta et al., 2019; Lv, Zhong, Wang, You, & Falco, 2023; Zhang, Cui, et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

Digital surface model generation from high‐resolution satellite stereos based on hybrid feature fusion network

Zheng,

Wan,

Zhang

et al. 2024

Self Cite

Recent studies have demonstrated that deep learning‐based stereo matching methods (DLSMs) can far exceed conventional ones on most benchmark datasets by both improving visual performance and decreasing the mismatching rate. However, applying DLSMs on high‐resolution satellite stereos with broad image coverage and wide terrain variety is still challenging. First, the broad coverage of satellite stereos brings a wide disparity range, while DLSMs are limited to a narrow disparity range in most cases, resulting in incorrect disparity estimation in areas with contradictory disparity ranges. Second, high‐resolution satellite stereos always comprise various terrain types, which is more complicated than carefully prepared datasets. Thus, the performance of DLSMs on satellite stereos is unstable, especially for intractable regions such as texture‐less and occluded regions. Third, generating DSMs requires occlusion‐aware disparity maps, while traditional occlusion detection methods are not always applicable for DLSMs with continuous disparity. To tackle these problems, this paper proposes a novel DLSM‐based DSM generation workflow. The workflow comprises three steps: pre‐processing, disparity estimation and post‐processing. The pre‐processing step introduces low‐resolution terrain to shift unmatched disparity ranges into a fixed scope and crops satellite stereos to regular patches. The disparity estimation step proposes a hybrid feature fusion network (HF2Net) to improve the matching performance. In detail, HF2Net designs a cross‐scale feature extractor (CSF) and a multi‐scale cost filter. The feature extractor differentiates structural‐context features in complex scenes and thus enhances HF2Net's robustness to satellite stereos, especially on intractable regions. The cost filter filters out most matching errors to ensure accurate disparity estimation. The post‐processing step generates initial DSM patches with estimated disparity maps and then refines them for the final large‐scale DSMs. Primary experiments on the public US3D dataset showed better accuracy than state‐of‐the‐art methods, indicating HF2Net's superiority. We then created a self‐made Gaofen‐7 dataset to train HF2Net and conducted DSM generation experiments on two Gaofen‐7 stereos to further demonstrate the effectiveness and practical capability of the proposed workflow.

“…Buildings have significant implications for urban development. Building extraction has facilitated numerous applications, including population density estimation, natural disaster prevention and warning, and urban planning (Freire et al., 2014; Griffiths & Boehm, 2019; Lafarge et al., 2008; Liu, Zhu, et al., 2023; Wei et al., 2004). Airborne light detection and ranging (LiDAR) has developed very rapidly in recent years and has become an attractive choice for building extraction, due to its high efficiency and measurement accuracy, lower interference by the external environment and strong initiative (Hui et al., 2021; Liu, Wang, et al., 2023; Zhou & Gong, 2018).…”

Section: Introductionmentioning

confidence: 99%

Building extraction from oblique photogrammetry point clouds based on PointNet++ with attention mechanism

Hu,

Tan,

Kang

et al. 2024

Unmanned aircraft vehicles (UAVs) capture oblique point clouds in outdoor scenes that contain considerable building information. Building features extracted from images are affected by the viewing point, illumination, occlusion, noise and image conditions, which make building features difficult to extract. Currently, ground elevation changes can provide powerful aids for the extraction, and point cloud data can precisely reflect this information. Thus, oblique photogrammetry point clouds have significant research implications. Traditional building extraction methods involve the filtering and sorting of raw data to separate buildings, which cause the point clouds to lose spatial information and reduce the building extraction accuracy. Therefore, we develop an intelligent building extraction method based on deep learning that incorporates an attention mechanism module into the Samling and PointNet operations within the set abstraction layer of the PointNet++ network. To assess the efficacy of our approach, we train and extract buildings from a dataset created using UAV oblique point clouds from five regions in the city of Bengbu, China. Impressive performance metrics are achieved, including 95.7% intersection over union, 96.5% accuracy, 96.5% precision, 98.7% recall and 97.8% F1 score. And with the addition of attention mechanism, the overall training accuracy of the model is improved by about 3%. This method showcases potential for advancing the accuracy and efficiency of digital urbanization construction projects.