TopoLAP: Topology Recovery for Building Reconstruction by Deducing the Relationships between Linear and Planar Primitives

Liu, Xinyi; Zhang, Yongjun; Ling, Xiao; Liu, Linyu; Li, Qian

doi:10.3390/rs11111372

Cited by 29 publications

(15 citation statements)

References 55 publications

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“…The complementary information also improves deep learning performance. The different topological method TopoLAB described in reference [17], that no longer uses neural networks, focuses on a pipeline to recover the broken topology of planar primitives during the reconstruction of complex building models. Due to the scanning difficulties and a variable point cloud density, some parts of a model can be missing.…”

Section: Related Workmentioning

confidence: 99%

Advanced Methods for Point Cloud Processing and Simplification

et al. 2020

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Nowadays, mobile robot exploration needs a rangefinder to obtain a large number of measurement points to achieve a detailed and precise description of a surrounding area and objects, which is called the point cloud. However, a single point cloud scan does not cover the whole area, so multiple point cloud scans must be acquired and compared together to find the right matching between them in a process called registration method. This method requires further processing and places high demands on memory consumption, especially for small embedded devices in mobile robots. This paper describes a novel method to reduce the burden of processing for multiple point cloud scans. We introduce our approach to preprocess an input point cloud in order to detect planar surfaces, simplify space description, fill gaps in point clouds, and get important space features. All of these processes are achieved by applying advanced image processing methods in combination with the quantization of physical space points. The results show the reliability of our approach to detect close parallel walls with suitable parameter settings. More importantly, planar surface detection shows a 99% decrease in necessary descriptive points almost in all cases. This proposed approach is verified on the real indoor point clouds.

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Section: Related Workmentioning

confidence: 99%

Advanced Methods for Point Cloud Processing and Simplification

et al. 2020

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“…Therefore, the topological consistency of the generated geometries is a major concern. The methodology is similar to the one proposed in TopoLAP (Liu et al, 2019). Even if the topology of planar and linear primitives is the primary purpose of this process also, the compactness of the models could limit their usability in small devices.…”

Section: Related Workmentioning

confidence: 99%

Automatic 3d Buildings Compact Reconstruction From Lidar Point Clouds

Nys¹,

Billen²,

Poux³

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Point clouds generated from aerial LiDAR and photogrammetric techniques are great ways to obtain valuable spatial insights over large scale. However, their nature hinders the direct extraction and sharing of underlying information. The generation of consistent large-scale 3D city models from this real-world data is a major challenge. Specifically, the integration in workflows usable by decision-making scenarios demands that the data is structured, rich and exchangeable. CityGML permits new advances in terms of interoperable endeavour to use city models in a collaborative way. Efforts have led to render good-looking digital twins of cities but few of them take into account their potential use in finite elements simulations (wind, floods, heat radiation model, etc.). In this paper, we target the automatic reconstruction of consistent 3D city buildings highlighting closed solids, coherent surface junctions, perfect snapping of vertices, etc. It specifically investigates the topological and geometrical consistency of generated models from aerial LiDAR point cloud, formatted following the CityJSON specifications. These models are then usable to store relevant information and provides geometries usable within complex computations such as computational fluid dynamics, free of local inconsistencies (e.g. holes and unclosed solids).

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“…An algorithm for identifying flat roofs and modeling individual buildings at the LoD3 level based on planar structures was proposed in [28]. A similar solution [29,30] for modeling buildings based on planar primitives produces structures with more elaborate shapes. Planar primitives are generated from a point cloud and are then reconstructed with the use of characteristic lines identified in the acquired images.…”

Section: Introductionmentioning

confidence: 99%

Modeling Multi-Rotunda Buildings at LoD3 Level from LiDAR Data

Kurdi

Lewandowicz²,

Gharineiat

et al. 2023

Remote Sensing

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The development of autonomous navigation systems requires digital building models at the LoD3 level. Buildings with atypically shaped features, such as turrets, domes, and chimneys, should be selected as landmark objects in these systems. The aim of this study was to develop a method that automatically transforms segmented LiDAR (Light Detection And Ranging) point cloud to create such landmark building models. A detailed solution was developed for selected buildings that are solids of revolution. The algorithm relies on new methods for determining building axes and cross-sections. To handle the gaps in vertical cross-sections due to the absence of continuous measurement data, a new strategy for filling these gaps was proposed based on their automatic interpretation. In addition, potential points associated with building ornaments were used to improve the model. The results were presented in different stages of the modeling process in graphic models and in a matrix recording. Our work demonstrates that complicated buildings can be represented with a light and regular data structure. Further investigations are needed to estimate the constructed building model with vectorial models.

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TopoLAP: Topology Recovery for Building Reconstruction by Deducing the Relationships between Linear and Planar Primitives

Cited by 29 publications

References 55 publications

Advanced Methods for Point Cloud Processing and Simplification

Advanced Methods for Point Cloud Processing and Simplification

Automatic 3d Buildings Compact Reconstruction From Lidar Point Clouds

Modeling Multi-Rotunda Buildings at LoD3 Level from LiDAR Data

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