Abstract: We propose a co-part segmentation method that takes a set of point clouds of the same category as input where neither a ground truth label nor a prior network is required. With difficulties ...

Abstract: In recent years, following the development of sensor and computer techniques, it is favored by many fields, i.e. automatic drive, intelligent home, etc., which the deep learning based ...

Abstract: Rapid and accurate segmentation of 3-D point clouds is critical for optimizing battery-swapping robots and ensuring precise assembly. To address the challenges of computational inefficiency ...

Abstract: Accurate environmental perception is critical for autonomous vehicles, typically achieved through multi-sensor fusion. However, existing camera-radar fusion methods often neglect effective ...

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Abstract: To address the limitations of insufficient geometric modeling and inadequate context fusion in indoor point cloud semantic segmentation, we propose Geometric-Relational Context Aggregation ...

Aquablue opened its new headquarters in Morristown, New Jersey, having expanded by 40%+ in 2025, strengthening their leadership and operational capabilities. As enterprises scale and digital ...

Abstract: In this letter, we consider a multi-stream carrier aggregation and coordinated multi-point enabled multi-input multi-output network, where each user can simultaneously associate with ...

Abstract: Federated learning empowers privacy-preserving, multi-party secure model training without the necessity of sharing raw data. In recent years, knowledge distillation has emerged as a ...

Abstract: Dear Editor, This letter focuses on the remaining useful life (RUL) prediction task under limited labeled samples. Existing machine-learning-based RUL prediction methods for this task ...