SHREC 2023: Point cloud change detection for city scenes

Gao, Yang; Yuan, Honglin; Ku, Tao; Veltkamp, Remco C.; Zamanakos, Georgios; Tsochatzidis, Lazaros; Amanatiadis, Angelos; Pratikakis, Ioannis; Panou, A.; Romanelis, Ioannis; Fotis, Vlassis; Arvanitis, Gerasimos; Μουστάκας, Κωνσταντίνος

doi:10.1016/j.cag.2023.06.025

Cited by 6 publications

(1 citation statement)

References 31 publications

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“…Almost all changes were detected, and approximately 80% of changes were correctly classified. In the work from Gao et al [29], they used a new labeled dataset to make an evaluation of point cloud change detection approaches. They used three Siamese neural networks, one based on geometry context aware (GCA), another using the voxel feature encoding architectures, and a third one using a Siamese KPConv network to detect changes between two point clouds at different chronological times.…”

Section: Detection Methods Based On Machine Learningmentioning

confidence: 99%

Change Detection in Point Clouds Using 3D Fractal Dimension

Casas-Rosa,

Navarro,

Segura-Sánchez

et al. 2024

Remote Sensing

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The management of large point clouds obtained by LiDAR sensors is an important topic in recent years due to the widespread use of this technology in a wide variety of applications and the increasing volume of data captured. One of the main applications of LIDAR systems is the study of the temporal evolution of the real environment. In open environments, it is important to know the evolution of erosive processes or landscape transformation. In the context of civil engineering and urban environments, it is useful for monitoring urban dynamics and growth, and changes during the construction of buildings or infrastructure facilities. The main problem with change detection (CD) methods is erroneous detection due to precision errors or the use of different capture devices at different times. This work presents a method to compare large point clouds, based on the study of the local fractal dimension of point clouds at multiple scales. Our method is robust in the presence of environmental and sensor factors that produce abnormal results with other methods. Furthermore, it is more stable than others in cases where there is no significant displacement of points but there is a local alteration of the structure of the point cloud. Furthermore, the precision can be adapted to the complexity and density of the point cloud. Finally, our solution is faster than other CD methods such as distance-based methods and can run at O(1) under some conditions, which is important when working with large datasets. All these improvements make the proposed method more suitable than the others to solve complex problems with LiDAR data, such as storage, time series data management, visualization, etc.

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Section: Detection Methods Based On Machine Learningmentioning

confidence: 99%