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 ...

QINGDAO, SHANDONG, CHINA, January 19, 2026 /EINPresswire.com/ — The landscape of environmental monitoring and analytical testing is witnessing significant ...

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: 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 ...

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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: Point cloud processing is fundamental to applications such as autonomous driving, robotic navigation, and 3D reconstruction. Sampling is a crucial process in point cloud processing, but ...

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 ...