REMOVED: SHREC 2021: 3D point cloud change detection for street scenes

Ku, Tao; Galanakis, Sam; Boom, Bas; Veltkamp, Remco C.; Bangera, Darshan; Gangisetty, Shankar; Stagakis, Nikolaos; Arvanitis, Gerasimos; Μουστάκας, Κωνσταντίνος

doi:10.1016/j.cag.2021.07.004

Cited by 27 publications

(22 citation statements)

References 20 publications

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“…In another work, Ref. [121] proposed a Siamese Graph Convolutional Network (SiamGCN) for 3D point clouds CD. The edge convolution (EdgeConv) operator is adopted to extract representative features from point clouds (Figure 10).…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Three Dimensional Change Detection Using Point Clouds: A Review

et al. 2022

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Change detection is an important step for the characterization of object dynamics at the earth’s surface. In multi-temporal point clouds, the main challenge is to detect true changes at different granularities in a scene subject to significant noise and occlusion. To better understand new research perspectives in this field, a deep review of recent advances in 3D change detection methods is needed. To this end, we present a comprehensive review of the state of the art of 3D change detection approaches, mainly those using 3D point clouds. We review standard methods and recent advances in the use of machine and deep learning for change detection. In addition, the paper presents a summary of 3D point cloud benchmark datasets from different sensors (aerial, mobile, and static), together with associated information. We also investigate representative evaluation metrics for this task. To finish, we present open questions and research perspectives. By reviewing the relevant papers in the field, we highlight the potential of bi- and multi-temporal point clouds for better monitoring analysis for various applications.

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Section: Deep Learning Methodsmentioning

confidence: 99%

Three Dimensional Change Detection Using Point Clouds: A Review

et al. 2022

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“…while Ku et al [37] and Li et al [38] directly use depth data for image-based change detection. Volumetric representations provide even greater context, allowing scene differencing for 3D change detection [39,40].…”

Section: B Changes In Scene Semanticsmentioning

confidence: 99%

3D VSG: Long-term Semantic Scene Change Prediction through 3D Variable Scene Graphs

Looper¹,

Rodríguez-Puigvert²,

Siegwart³

et al. 2022

Preprint

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Numerous applications require robots to operate in environments shared with other agents such as humans or other robots. However, such shared scenes are typically subject to different kinds of long-term semantic scene changes. The ability to model and predict such changes is thus crucial for robot autonomy. In this work, we formalize the task of semantic scene variability estimation and identify three main varieties of semantic scene change: changes in the position of an object, its semantic state, or the composition of a scene as a whole.To represent this variability, we propose the Variable Scene Graph (VSG), which augments existing 3D Scene Graph (SG) representations with the variability attribute, representing the likelihood of discrete long-term change events. We present a novel method, DeltaVSG, to estimate the variability of VSGs in a supervised fashion. We evaluate our method on the 3RScan long-term dataset, showing notable improvements in this novel task over existing approaches. Our method DeltaVSG achieves a precision of 72.2% and recall of 66.8%, often mimicking human intuition about how indoor scenes change over time. We further show the utility of VSG predictions in the task of active robotic change detection, speeding up task completion by 62.4% compared to a scene-change-unaware planner. We make our code available as open-source.

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“…Indeed, one can cite the work of [17], [18] which apply deep models to retrieve changes, but they only focus on 2.5D DSM. On the contrary, [19] propose to process the raw 3D PCs thanks to graph convolutions [20]. However, their method is designed for change classification task, i.e., retrieving changes at scene level as proposed by the Change3D [19] or Urban 3D Change Detection Classification (Urb3DCD-Cls) [14] datasets.…”

mentioning

confidence: 99%

“…On the contrary, [19] propose to process the raw 3D PCs thanks to graph convolutions [20]. However, their method is designed for change classification task, i.e., retrieving changes at scene level as proposed by the Change3D [19] or Urban 3D Change Detection Classification (Urb3DCD-Cls) [14] datasets. This task is less precise than the change segmentation one, since it allows identifying only the main changed object in a scene without precisely localizing it.…”

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confidence: 99%

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Change Detection in Urban Point Clouds: An Experimental Comparison with Simulated 3D Datasets

2021

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In the context of rapid urbanization, monitoring the evolution of cities is crucial. To do so, 3D change detection and characterization is of capital importance since, unlike 2D images, 3D data contain vertical information of utmost importance to monitoring city evolution (that occurs along both horizontal and vertical axes). Urban 3D change detection has thus received growing attention, and various methods have been published on the topic. Nevertheless, no quantitative comparison on a public dataset has been reported yet. This study presents an experimental comparison of six methods: three traditional (difference of DSMs, C2C and M3C2), one machine learning with hand-crafted features (a random forest model with a stability feature) and two deep learning (feed-forward and Siamese architectures). In order to compare these methods, we prepared five sub-datasets containing simulated pairs of 3D annotated point clouds with different characteristics: from high to low resolution, with various levels of noise. The methods have been tested on each sub-dataset for binary and multi-class segmentation. For supervised methods, we also assessed the transfer learning capacity and the influence of the training set size. The methods we used provide various kinds of results (2D pixels, 2D patches or 3D points), and each of them is impacted by the resolution of the PCs. However, while the performances of deep learning methods highly depend on the size of the training set, they seem to be less impacted by training on datasets with different characteristics. Oppositely, conventional machine learning methods exhibit stable results, even with smaller training sets, but embed low transfer learning capacities. While the main changes in our datasets were usually identified, there were still numerous instances of false detection, especially in dense urban areas, thereby calling for further development in this field. To assist such developments, we provide a public dataset composed of pairs of point clouds with different qualities together with their change-related annotations. This dataset was built with an original simulation tool which allows one to generate bi-temporal urban point clouds under various conditions.

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REMOVED: SHREC 2021: 3D point cloud change detection for street scenes

Cited by 27 publications

References 20 publications

Three Dimensional Change Detection Using Point Clouds: A Review

Three Dimensional Change Detection Using Point Clouds: A Review

3D VSG: Long-term Semantic Scene Change Prediction through 3D Variable Scene Graphs

Change Detection in Urban Point Clouds: An Experimental Comparison with Simulated 3D Datasets

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