Journal Article10.1016/J.ROBOT.2013.07.001
Three-dimensional point cloud plane segmentation in both structured and unstructured environments
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TL;DR: These algorithms have been evaluated using real-world datasets from both structured and unstructured environments and benchmarked against a state-of-the-art point-based region growing (PBRG) algorithm with regard to segmentation speed.
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About: This article is published in Robotics and Autonomous Systems. The article was published on 01 Dec 2013. The article focuses on the topics: Region growing & Point cloud.
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Citations
Linking Points With Labels in 3D: A Review of Point Cloud Semantic Segmentation
TL;DR: In this article, the authors summarized available data sets and relevant studies on recent developments in point cloud semantic segmentation and point cloud segmentation (PCS) for 3D point clouds.
A review ofpoint clouds segmentation and classification algorithms
TL;DR: The most popular methodologies and algorithms to segment and classify 3D point clouds are analyzed to provide 3D data with meaningful attributes that characterize and provide significance to the objects represented in 3D.
Registration of large-scale terrestrial laser scanner point clouds: A review and benchmark
Zhen Dong,Fuxun Liang,Bisheng Yang,Yusheng Xu,Yufu Zang,Jianping Li,Yuan Wang,Wenxia Dai,Hongchao Fan,Juha Hyyppä,Uwe Stilla +10 more
TL;DR: A thorough review of terrestrial laser scanner point cloud registration methods in terms of pairwise coarse registration, pairwise fine registration, and multiview registration, as well as analyzing their strengths, weaknesses, and future research trends are conducted.
341
An Improved RANSAC for 3D Point Cloud Plane Segmentation Based on Normal Distribution Transformation Cells
TL;DR: An improved RANSAC method based on Normal Distribution Transformation (NDT) cells is proposed in this study to avoid spurious planes for 3D point-cloud plane segmentation and is verified on three indoor scenes to validate the suitability of the method.
261
Computational Methods of Acquisition and Processing of 3D Point Cloud Data for Construction Applications
Qian Wang,Yi Tan,Zhongya Mei +2 more
TL;DR: The state-of-the-art methods to acquire and process 3D point cloud data for construction applications are reviewed and the different processing methods and algorithms are compared and discussed in detail, which provides a useful guidance to both researchers and industry practitioners for adopting point cloudData in the construction industry.
182
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TL;DR: PCL (Point Cloud Library) is presented, an advanced and extensive approach to the subject of 3D perception that contains state-of-the art algorithms for: filtering, feature estimation, surface reconstruction, registration, model fitting and segmentation.
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Zhengyou Zhang
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TL;DR: In this article, a least-squares technique is used to estimate 3D motion from the point correspondences, which reduces the average distance between curves in two sets, and yields an accurate motion estimate.
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TL;DR: First results on real data demonstrate, that the normal distributions transform algorithm is capable to map unmodified indoor environments reliable and in real time, even without using odometry data.
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