Abstract: With the focus on three-dimensional (3D) applications, the importance of applying deep learning to point clouds have been growing recently. It is known that mapping operations including ...
Abstract: We propose an end-to-end attribute compression method for dense point clouds. The proposed method combines a frequency sampling module, an adaptive scale feature extraction module with ...
Abstract: Local sampling plays a key role in modeling 3D point clouds. Due to the disordered and unstructured nature of point cloud data, conventional 3D deep models such as PointNet++ and its ...
Abstract: Given that the sampling and arrangement of the array need a reasonable standard to guide in high-precision magnetocardiography (MCG) imaging, the sampling range required for robust ...
Abstract: Low sampling frequency challenges the exact identification of continuous-time (CT) dynamical systems from sampled data, even when their models are identifiable. A necessary and sufficient ...
Abstract: Implicit Neural Representations (INRs), capable of learning the mapping between sampling coordinates and occupancy statuses to effectively represent 3D content, are increasingly adopted in ...
Abstract: The increasing adoption of advanced three-dimensional (3D) scanning technologies has made large-scale point clouds containing millions of 3D measurement points standard in applications like ...
Abstract: Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks continue to pose significant threats to networked systems, causing disruptions that can lead to substantial financial ...
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