City-scale Scene Change Detection using Point Clouds

Yew, Zi Jian; Lee, Gim Hee

doi:10.1109/icra48506.2021.9561855

Cited by 11 publications

(3 citation statements)

References 40 publications

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“…More recently, the state-of-the-art includes methods focusing on the comparison of three-dimensional point cloud pairs, taken at different times (Yew and Lee, 2021). In the latter paper the authors infer, using a neural network, a non-rigid transform to temporally align and account for point cloud inconsistencies.…”

Section: Related Workmentioning

confidence: 99%

Semantic-aware Spatiotemporal Alignment of Natural Outdoor Surveys

Chahine¹,

Pradalier²

2022

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This article presents a keyframe-based, innovative map registration scheme for applications that benefit from recurring data acquisition, such as long-term natural environment monitoring. The proposed method consists of a multistage pipeline, in which semantic knowledge of the scene is acquired using a pretrained neural network. The semantic knowledge is subsequently employed to constrain the Iterative Closest Point algorithm (ICP). In this article, semantic-aware ICP is used to build keyframes as well as to align them both spatially and temporally, with neighboring keyframes and those captured around the same area but at a different point in time, respectively. Hierarchical clustering of ICP-generated transformations is then used to both eliminate outliers and find alignment consensus, followed by an optimization scheme based on a factor graph that includes loop closure. To evaluate the proposed framework, data were captured using a portable robotic sensor suite consisting of three cameras, a three-dimensional lidar, and an inertial navigation system. The data were acquired monthly over 12 months by revisiting the same trajectory between August 2020 and July 2021.

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Section: Related Workmentioning

confidence: 99%

Semantic-aware Spatiotemporal Alignment of Natural Outdoor Surveys

Chahine¹,

Pradalier²

2022

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“…Alternative solutions rely on structure from motion (SfM) and multi-view stereo (MVS) to generate point clouds [16,17]. However, these methods are time-consuming and require several days to generate point clouds for a medium-sized urban environment [18].…”

Section: Introductionmentioning

confidence: 99%

SHREC 2023: Point cloud change detection for city scenes

Gao

Yuan

et al. 2023

Computers & Graphics

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“…Pang et al [5] created a 3D change map using a deep convolution network in addition to a graph-based and simultaneous segmentation method. Yew and Lee [45] applied a convolution network to balanced point clouds obtained from "structure from motion" of two times for urban CD. In another study, Yadav et al [46] used Lidar point clouds for 3D building CD in the Unet network.…”

Section: Introductionmentioning

confidence: 99%

Automatic 3D Multiple Building Change Detection Model Based on Encoder–Decoder Network Using Highly Unbalanced Remote Sensing Datasets

Gomroki,

Hasanlou,

Chanussot

2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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3D building change detection (CD) methods detect more accurate multiple change maps than 2D ones. Recent technologies such as unmanned aerial vehicle (UAV) systems and dense image matching have made it much easier to obtain 3D data nowadays. Developing a solution which produces an accurate map of multiple building changes, including Unclassified, No building change, Newly built, Demolished, and Taller, at an acceptable speed is a challenging issue. In this study we address a novel 3D building CD method based on an Encoder-Decoder network to detect accurate multiple changes maps automatically, in the presence of highly unbalanced remote sensing datasets. The proposed method consists of three main parts: the pre-processing and mixed augmentation (MA) step, the Encoder-Decoder network training, and finally the prediction step. The data are augmented by the MA method to manipulate highly unbalanced datasets. The Encoder-Decoder network is constructed by the Yolov7 network as the encoder path and the decoder path equipped with the convolutional layers of modified Unet (CLMUnet). Two datasets are used in this study. The first dataset is the point clouds and orthophotos obtained from the UAV of Mashhad City in 2011 and 2016. The second dataset consists of stereo images of the GeoEye-1 satellite and the point clouds obtained from dense image matching of Tehran city in 2009 and 2013. The results show that the proposed method achieved accuracy and kappa coefficients above 94% and 0.90 for both datasets, respectively.

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City-scale Scene Change Detection using Point Clouds

Cited by 11 publications

References 40 publications

Semantic-aware Spatiotemporal Alignment of Natural Outdoor Surveys

Semantic-aware Spatiotemporal Alignment of Natural Outdoor Surveys

SHREC 2023: Point cloud change detection for city scenes

Automatic 3D Multiple Building Change Detection Model Based on Encoder–Decoder Network Using Highly Unbalanced Remote Sensing Datasets

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