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2022 ◽  
Vol 134 ◽  
pp. 104065
Paulina Stałowska ◽  
Czesław Suchocki ◽  
Miłosława Rutkowska

2022 ◽  
Vol 134 ◽  
pp. 104096
Shoujun Jia ◽  
Chun Liu ◽  
Xianjun Guan ◽  
Hangbin Wu ◽  
Doudou Zeng ◽  

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 263
Munan Yuan ◽  
Xiru Li ◽  
Longle Cheng ◽  
Xiaofeng Li ◽  
Haibo Tan

Alignment is a critical aspect of point cloud data (PCD) processing, and we propose a coarse-to-fine registration method based on bipartite graph matching in this paper. After data pre-processing, the registration progress can be detailed as follows: Firstly, a top-tail (TT) strategy is designed to normalize and estimate the scale factor of two given PCD sets, which can combine with the coarse alignment process flexibly. Secondly, we utilize the 3D scale-invariant feature transform (3D SIFT) method to extract point features and adopt fast point feature histograms (FPFH) to describe corresponding feature points simultaneously. Thirdly, we construct a similarity weight matrix of the source and target point data sets with bipartite graph structure. Moreover, the similarity weight threshold is used to reject some bipartite graph matching error-point pairs, which determines the dependencies of two data sets and completes the coarse alignment process. Finally, we introduce the trimmed iterative closest point (TrICP) algorithm to perform fine registration. A series of extensive experiments have been conducted to validate that, compared with other algorithms based on ICP and several representative coarse-to-fine alignment methods, the registration accuracy and efficiency of our method are more stable and robust in various scenes and are especially more applicable with scale factors.

Quaternary ◽  
2022 ◽  
Vol 5 (1) ◽  
pp. 5
Matthew D. Howland ◽  
Anthony Tamberino ◽  
Ioannis Liritzis ◽  
Thomas E. Levy

This paper tests the suitability of automated point cloud classification tools provided by the popular image-based modeling (IBM) software package Agisoft Metashape for the generation of digital terrain models (DTMs) at moderately-vegetated archaeological sites. DTMs are often required for various forms of archaeological mapping and analysis. The suite of tools provided by Agisoft are relatively user-friendly as compared to many point cloud classification algorithms and do not require the use of additional software. Based on a case study from the Mycenaean site of Kastrouli, Greece, the mostly-automated, geometric classification tool “Classify Ground Points” provides the best results and produces a quality DTM that is sufficient for mapping and analysis. Each of the methods tested in this paper can likely be improved through manual editing of point cloud classification.

2022 ◽  
Vol 14 (2) ◽  
pp. 367
Zhen Zheng ◽  
Bingting Zha ◽  
Yu Zhou ◽  
Jinbo Huang ◽  
Youshi Xuchen ◽  

This paper proposes a single-stage adaptive multi-scale noise filtering algorithm for point clouds, based on feature information, which aims to mitigate the fact that the current laser point cloud noise filtering algorithm has difficulty quickly completing the single-stage adaptive filtering of multi-scale noise. The feature information from each point of the point cloud is obtained based on the efficient k-dimensional (k-d) tree data structure and amended normal vector estimation methods, and the adaptive threshold is used to divide the point cloud into large-scale noise, a feature-rich region, and a flat region to reduce the computational time. The large-scale noise is removed directly, the feature-rich and flat regions are filtered via improved bilateral filtering algorithm and weighted average filtering algorithm based on grey relational analysis, respectively. Simulation results show that the proposed algorithm performs better than the state-of-art comparison algorithms. It was, thus, verified that the algorithm proposed in this paper can quickly and adaptively (i) filter out large-scale noise, (ii) smooth small-scale noise, and (iii) effectively maintain the geometric features of the point cloud. The developed algorithm provides research thought for filtering pre-processing methods applicable in 3D measurements, remote sensing, and target recognition based on point clouds.

2022 ◽  
Vol 10 (1) ◽  
pp. 101
Ante Šiljeg ◽  
Ivan Marić ◽  
Fran Domazetović ◽  
Neven Cukrov ◽  
Marin Lovrić ◽  

Multibeam echosounders (MBES) have become a valuable tool for underwater floor mapping. However, MBES data are often loaded with different measurement errors. This study presents a new user-friendly and methodological semi-automatic approach of point cloud post-processing error removal. The St. Anthony Channel (Croatia) was selected as the research area because it is regarded as one of the most demanding sea or river passages in the world and it is protected as a significant landscape by the Šibenik-Knin County. The two main objectives of this study, conducted within the Interreg Italy–Croatia PEPSEA project, were to: (a) propose a methodological framework that would enable the easier and user-friendly identification and removal of the errors in MBES data; (b) create a high-resolution integral model (MBES and UAV data) of the St. Anthony Channel for maritime safety and tourism promotion purposes. A hydrographic survey of the channel was carried out using WASSP S3 MBES while UAV photogrammetry was performed using Matrice 210 RTK V2. The proposed semi-automatic post-processing of the MBES acquired point cloud was completed in the Open Source CloudCompare software following five steps in which various point filtering methods were used. The reduction percentage in points after the denoising process was 14.11%. Our results provided: (a) a new user-friendly methodological framework for MBES point filtering; (b) a detailed bathymetric map of the St. Anthony Channel with a spatial resolution of 50 cm; and (c) the first integral (MBES and UAV) high-resolution model of the St. Anthony Channel. The generated models can primarily be used for maritime safety and tourism promotion purposes. In future research, ground-truthing methods (e.g., ROVs) will be used to validate the generated models.

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