Direct Co-Registration of Tir Images and MLS Point Clouds by Corresponding Keypoints

Zhu, Jingwei; Xu, Yangsheng; Hoegner, L.; Stilla, Uwe

doi:10.5194/isprs-annals-iv-2-w7-235-2019

Cited by 3 publications

(1 citation statement)

References 24 publications

(28 reference statements)

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“…Furthermore, combining data from thermal imagery allows for thermal attribute mapping [149][150][151], while multispectral information can help assess the conservation state of a building [152]. An interesting example of multimodal data fusion is reported by Jarz ąbek-Rychard et al [153].…”

Section: Data Fusion For Scene Interpretationmentioning

confidence: 99%

Detailed Three-Dimensional Building Façade Reconstruction: A Review on Applications, Data and Technologies

et al. 2022

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Urban environments are regions of complex and diverse architecture. Their reconstruction and representation as three-dimensional city models have attracted the attention of many researchers and industry specialists, as they increasingly recognise the potential for new applications requiring detailed building models. Nevertheless, despite being investigated for a few decades, the comprehensive reconstruction of buildings remains a challenging task. While there is a considerable body of literature on this topic, including several systematic reviews summarising ways of acquiring and reconstructing coarse building structures, there is a paucity of in-depth research on the detection and reconstruction of façade openings (i.e., windows and doors). In this review, we provide an overview of emerging applications, data acquisition and processing techniques for building façade reconstruction, emphasising building opening detection. The use of traditional technologies from terrestrial and aerial platforms, along with emerging approaches, such as mobile phones and volunteered geography information, is discussed. The current status of approaches for opening detection is then examined in detail, separated into methods for three-dimensional and two-dimensional data. Based on the review, it is clear that a key limitation associated with façade reconstruction is process automation and the need for user intervention. Another limitation is the incompleteness of the data due to occlusion, which can be reduced by data fusion. In addition, the lack of available diverse benchmark datasets and further investigation into deep-learning methods for façade openings extraction present crucial opportunities for future research.

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Section: Data Fusion For Scene Interpretationmentioning

confidence: 99%

Detailed Three-Dimensional Building Façade Reconstruction: A Review on Applications, Data and Technologies

et al. 2022

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Toward Building and Civil Infrastructure Reconstruction From Point Clouds: A Review on Data and Key Techniques

Stilla

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Uniform and Competency-Based 3D Keypoint Detection for Coarse Registration of Point Clouds with Homogeneous Structure

et al. 2022

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Recent advances in 3D laser scanner technology have provided a large amount of accurate geo-information as point clouds. The methods of machine vision and photogrammetry are used in various applications such as medicine, environmental studies, and cultural heritage. Aerial laser scanners (ALS), terrestrial laser scanners (TLS), mobile mapping laser scanners (MLS), and photogrammetric cameras via image matching are the most important tools for producing point clouds. In most applications, the process of point cloud registration is considered to be a fundamental issue. Due to the high volume of initial point cloud data, 3D keypoint detection has been introduced as an important step in the registration of point clouds. In this step, the initial volume of point clouds is converted into a set of candidate points with high information content. Many methods for 3D keypoint detection have been proposed in machine vision, and most of them were based on thresholding the saliency of points, but less attention had been paid to the spatial distribution and number of extracted points. This poses a challenge in the registration process when dealing with point clouds with a homogeneous structure. As keypoints are selected in areas of structural complexity, it leads to an unbalanced distribution of keypoints and a lower registration quality. This research presents an automated approach for 3D keypoint detection to control the quality, spatial distribution, and the number of keypoints. The proposed method generates a quality criterion by combining 3D local shape features, 3D local self-similarity, and the histogram of normal orientation and provides a competency index. In addition, the Octree structure is applied to control the spatial distribution of the detected 3D keypoints. The proposed method was evaluated for the keypoint-based coarse registration of aerial laser scanner and terrestrial laser scanner data, having both cluttered and homogeneous regions. The obtained results demonstrate the proper performance of the proposed method in the registration of these types of data, and in comparison to the standard algorithms, the registration error was diminished by up to 56%.

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Direct Co-Registration of Tir Images and MLS Point Clouds by Corresponding Keypoints

Cited by 3 publications

References 24 publications

Detailed Three-Dimensional Building Façade Reconstruction: A Review on Applications, Data and Technologies

Detailed Three-Dimensional Building Façade Reconstruction: A Review on Applications, Data and Technologies

Toward Building and Civil Infrastructure Reconstruction From Point Clouds: A Review on Data and Key Techniques

Uniform and Competency-Based 3D Keypoint Detection for Coarse Registration of Point Clouds with Homogeneous Structure

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