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Showing 1 to 4 of 4 for “"Point Cloud Datasets"”.
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Automated Geometric Digital Twin Construction for Existing Buildings from Point Cloud Datasets
… DTs or gDTs). This process involves capturing Point Cloud Datasets (PCDs) and modelling these datasets to represent current building geometry accurately. However, this process requires extensive manual labour and remains a barrier to the broader adoption of DTs for the operation and maintenance …
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Unsupervised Feature Learning for Point Cloud by Contrasting and Clustering with Graph Convolutional Neural Network
… (GNNs) have attracted significant attention for point cloud understanding tasks, including classification, segmentation, and detection. However, the training of such deep networks still requires a large amount of annotated data, which is both expensive and time-consuming. To alleviate the cost of …
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SnapshotNet: Self-supervised Feature Learning for Point Cloud Data Segmentation Using Minimal Labeled Data
<p>Manually annotating complex scene point cloud datasets is both costly and error-prone. To reduce the reliance on labeled data, a new model called SnapshotNet is proposed as a self-supervised feature learning approach, which directly works on the unlabeled point cloud data of a complex 3D scene. …
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Automatic Alignment of 3D Multi-Sensor Point Clouds
Automatic 3D point cloud alignment is a major research topic in photogrammetry, computer vision and computer graphics. In this research, two keypoint feature matching approaches have been developed and proposed for the automatic alignment of 3D point clouds, which have been acquired from different …