Pole-Like Road Furniture Detection in Sparse and Unevenly Distributed Mobile Laser Scanning Data

Li, Fashuai; Lehtomäki, Matti; Elberink, Sander Oude; Vosselman, G.; Kukko, Antero; Hyyppä, Juha

doi:10.5194/isprs-annals-iv-2-185-2018

Cited by 6 publications

(4 citation statements)

References 22 publications

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“…A hierarchical strategy composed of rules and multi-scale supervoxels was proposed in [10] to recognise roadside objects in MLS data. In [11], a slice-cut method was proposed to identify road poles in MLS data. The method combined the co-axial cylinder fitting model and slice-wise features, which is robust to both sparse and dense point clouds.…”

Section: A Model-driven Methodsmentioning

confidence: 99%

Instance-Aware Semantic Segmentation of Road Furniture in Mobile Laser Scanning Data

Zhou

Xiao³

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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In this paper, we present an improved framework for the instance-aware semantic segmentation of road furniture in mobile laser scanning data. In our framework, we first detect road furniture from mobile laser scanning point clouds. Then we decompose the detected pieces of road furniture into poles and their attached components, and extract the instance information of the components with different features. Most importantly, we classify the components into different categories by combining a classifier and a probabilistic graphic model named DenseCRF, which is the major contribution of this paper. For the classification of the components using DenseCRF, the unary potentials and the pairwise potentials are first obtained. The unary potentials are obtained from the classifier which takes the instance information of components as the input. The pairwise potentials are calculated considering contextual relations between components. By utilising DenseCRF, the contextual consistency of components is preserved, and the performance is significantly improved compared to our previous work. We collect three datasets to test our framework, and compare the classification performances of six different classifiers with and without DenseCRF. The Manuscript

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Section: A Model-driven Methodsmentioning

confidence: 99%

Instance-Aware Semantic Segmentation of Road Furniture in Mobile Laser Scanning Data

Zhou

Xiao³

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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“…Fortunately, a building façade can be usually assumed as a set of planar surfaces while a pole-like object is more like a linear shape such as a cylinder. One simple approach to tackle it is to remove the building façade by detecting large planar surfaces (Li, et al [ 114 ]). Many approaches apply PCA, which has been demonstrated to be an effective approach to describe the linearity of a cluster of points, to detect linear objects (e.g., Teo and Chiu [ 100 ], Guan, et al [ 104 ], Yadav, et al [ 106 ], El-Halawany and Lichti [ 111 ]).…”

Section: Object Recognitionmentioning

confidence: 99%

Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review

Che

Jung

2019

Sensors

203

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Mobile Laser Scanning (MLS) is a versatile remote sensing technology based on Light Detection and Ranging (lidar) technology that has been utilized for a wide range of applications. Several previous reviews focused on applications or characteristics of these systems exist in the literature, however, reviews of the many innovative data processing strategies described in the literature have not been conducted in sufficient depth. To this end, we review and summarize the state of the art for MLS data processing approaches, including feature extraction, segmentation, object recognition, and classification. In this review, we first discuss the impact of the scene type to the development of an MLS data processing method. Then, where appropriate, we describe relevant generalized algorithms for feature extraction and segmentation that are applicable to and implemented in many processing approaches. The methods for object recognition and point cloud classification are further reviewed including both the general concepts as well as technical details. In addition, available benchmark datasets for object recognition and classification are summarized. Further, the current limitations and challenges that a significant portion of point cloud processing techniques face are discussed. This review concludes with our future outlook of the trends and opportunities of MLS data processing algorithms and applications.

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“…At the end of this stage, we extract pole-like road furniture by cutting the remainder above-ground objects into slices and performing 2D connected components analysis with the centre points of these slices. The details of this pole-like road furniture detection can be found in (Li et al, 2018b). Fig.…”

Section: Pole-like Road Furniture Detectionmentioning

confidence: 99%

Semantic segmentation of road furniture in mobile laser scanning data

Lehtomäki

Elberink

et al. 2019

ISPRS Journal of Photogrammetry and Remote Sensing

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Pole-Like Road Furniture Detection in Sparse and Unevenly Distributed Mobile Laser Scanning Data

Cited by 6 publications

References 22 publications

Instance-Aware Semantic Segmentation of Road Furniture in Mobile Laser Scanning Data

Instance-Aware Semantic Segmentation of Road Furniture in Mobile Laser Scanning Data

Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review

Semantic segmentation of road furniture in mobile laser scanning data

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