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Abstract: With the development of LiDAR detection technology, LiDAR point clouds have been applied in many fields such as surveying and mapping production, autonomous driving, and smart cities.

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Abstract: At present, with the original point cloud as input, most of the object detectors use Pointnet++ to extract features of the point cloud based on the Farthest Point Sampling (FPS). However, ...

Abstract: The development of Internet of Things (IoT) technology in the field of intelligent vehicle, has raised requirements on the measurement effect of automotive millimeter wave (MMW) radar sensor ...

Abstract: The task of completing shapes of complex curved surface components is a challenging yet crucial problem in industrial quality inspection. The hollow structures, irregular sectional profiles, ...

Abstract: Three-dimensional (3D) point clouds are essential for representing traffic scenes in autonomous driving, yet incompletion often occurs due to sensor angle limitations and signal occlusions.

Abstract: Existing multimodal point cloud quality assessment (PCQA) methods usually integrate 3D and 2D information to simulate human visual perception of distortions. However, due to the lack of ...