Automatic Detection and Vectorization of Linear and Point Objects in 3d Point Cloud and Panoramic Images From Mobile Mapping System

Barçon, E.; Picard, Andrew L.

doi:10.5194/isprs-archives-xliii-b2-2021-305-2021

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…The method allows to generate IFC model of the road using predefined 3D customizable models. In a previous work, Barçon and Picard (2021) propose two different workflows aiming to extract road features from MLS data. The first one describes a process that extracts and vectorizes pole like objects in an urban context, whereas the second one performs the extraction of road markings and guardrails in a high-way environment.…”

Section: Road Features Inventorymentioning

confidence: 99%

Vectorization of Urban MLS Point Clouds: A Sequential Approach Using Cross Sections

Barçon¹,

Landes

Grussenmeyer

et al. 2022

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

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Abstract. Dense point clouds acquired with a mobile laser scanning system (MLS) device become usual raw data for different surveyor applications: topographic maps, 3D models, road inventories, risk assessment of vegetation on road or railroads. Thanks to important evolutions in technologies, MLS devices became powerful and very popular. In the meantime, the need for point cloud automatic processing tools is growing. However, the available tools have not yet reached a sufficient level of maturity. Using MLS point clouds to produce topographic maps, BIM model or other deliverables, requires very often manual vectorization (or digitalization) work. In the road context, the transition from point cloud to road map that consists in delineating curb or road edges, road markings, pole, trees, facades etc. is currently performed manually. To reduce these time-consuming operations, several solutions have been proposed in the literature. In this paper we present the first results of a method consisting in vectorizing urban point cloud scene. The originality of this work is to propose a global approach aiming to detect and vectorize simultaneously multiple objects. The developed algorithm uses cross-section analysis to detect road curbs and vertical objects. The first results are promising, since an F-score higher than 80% has been reached, even before applying road logic rules or additional knowledge. The detection and extraction of vertical objects including facades, trees, and poles, is more challenging but the detections also present a recall greater than 85%.

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Section: Road Features Inventorymentioning

confidence: 99%

Vectorization of Urban MLS Point Clouds: A Sequential Approach Using Cross Sections

Barçon¹,

Landes

Grussenmeyer

et al. 2022

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

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show abstract

“…Different rules, defined as angle or distance threshold allow to do or do not merge two segments. Barçon and Picard (2021), Jung et al (2019), Yao et al (2021) proposed similar geometric considerations to perform a lane-line reconstruction from individual dash-lines. Taking a first marking, an azimuth search direction is defined.…”

Section: Shape Reconstruction and Vectorizationmentioning

confidence: 99%

Extraction of Road Markings From MLS Data: A Review

Barçon¹,

Landes

Grussenmeyer

et al. 2022

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

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Abstract. Nowadays, Mobile Laser Scanning (MLS) systems are more and more used to realize extended topographic surveys of roads. Most of them provide for each measured point an attribute corresponding to a return signal strength, so called intensity value. This value enables to easily understand uncolored MLS as it helps to differentiate materials based on their albedo. In a road context, this intensity information allows to distinguish, among others, the main subject of this paper, i.e. road markings. However, this task is challenging. Road marking detection from dense MLS point cloud is widely studied by the research community. It might concern road management and diagnosis, intelligent traffic systems, high-definition maps, location and navigation services. Dense MLS point clouds provided by surveyors are not processed online, they are thus not directly applicable to autonomous driving, but those dense and precise data can be for instance used for the generation of HD reference maps. This paper presents a review of the different processing chains published in the literature. It underlines their contributions and highlights their potential limitations. Finally, a discussion and some suggestions of improvement are given.

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Accurate Interior 3d Modelling Sans Control Points: Enhance Iphone Tof Capability

amooshahi,

Milan,

Shojaei

2024

Preprint

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Automatic Detection and Vectorization of Linear and Point Objects in 3d Point Cloud and Panoramic Images From Mobile Mapping System

Cited by 3 publications

References 21 publications

Vectorization of Urban MLS Point Clouds: A Sequential Approach Using Cross Sections

Vectorization of Urban MLS Point Clouds: A Sequential Approach Using Cross Sections

Extraction of Road Markings From MLS Data: A Review

Accurate Interior 3d Modelling Sans Control Points: Enhance Iphone Tof Capability

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