Representing point clouds with generative conditional invertible flow networks

Stypułkowski, Michał; Kania, Kacper; Zamorski, Maciej; Zięba, Maciej; Trzciński, Tomasz Piotr; Chorowski, Jan

doi:10.1016/j.patrec.2021.07.001

Cited by 4 publications

(1 citation statement)

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“…Some researchers [36][37][38] studied the reconstruction of 3D models, which obtains the spatial coordinates of each point on the component surface. Then, the spatial information is transformed into digital information that can be directly processed using computing equipment.…”

Section: Introductionmentioning

confidence: 99%

A Terrestrial Laser Scanning-Based Method for Indoor Geometric Quality Measurement

Tan,

Liu,

Jin

et al. 2023

Remote Sensing

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The indoor geometric dimensions of a building are crucial for acceptance criteria. Traditional manual methods for measuring indoor geometric quality are labor-intensive, time-consuming, error-prone, and yield non-reproducible results. With the advancement of ground-based laser scanning technology, the efficient and precise measurement of geometric dimensions has become achievable. An indoor geometric quality measurement method based on ground-based laser scanning is presented in this paper. Initially, a coordinate transformation algorithm based on selected points was developed for conducting coordinate conversion. Subsequently, the Cube Diagonal-based Denoising algorithm, developed for point cloud denoising, was employed. Following that, architectural components such as walls, ceilings, floors, and openings were identified and extracted based on their spatial relationships. The measurement and visualization of the geometric quality of walls’ flatness, verticality, and opening dimensions were automated using fitting and simulation methods. Lastly, tests and validation were conducted to assess the accuracy and applicability of the proposed method. The experimental results demonstrate that time and human resources can be significantly saved using this method. The accuracy of this method in assessing wall flatness, verticality, and opening dimensions is 77.8%, 88.9%, and 95.9%, respectively. These results indicate that indoor geometric quality can be detected more accurately and efficiently compared to traditional inspection methods using the proposed method.

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Section: Introductionmentioning

confidence: 99%