Abstract: Point cloud classification is highly dependent on how points' features are extracted and aggregated. The graph-based feature extraction strategies are currently used. Not only point ...

Abstract: Point cloud compression is critical to deploy 3D applications like autonomous driving. However, LiDAR point clouds contain many disconnected regions, where redundant bits for unoccupied 3D ...

Java map() vs flatMap() — Learn the key differences, when to use each, and how to avoid nested Optional and Stream structures. With full runnable examples.

Abstract: Effective sampling plays a critical role in the preprocessing of 3D point cloud data, directly impacting the performance of downstream models. Traditional Farthest Point Sampling (FPS) ...

Abstract: Ensuring precise segmentation of point clouds is essential for intelligent inspection in transmission line corridors. The massive scale, unordered distribution, and complex structures of ...

Learn how to make simple apps in Android Studio. Android Studio Tutorials: Java Edition provides practical examples and complete source code to help you build your first Android application using ...

Abstract: Multipoint dynamic aggregation (MPDA) is a multirobot task allocation problem, which requires the collaborative scheduling of multiple robots to complete time-varying tasks distributed on a ...

Abstract: Existing methods for learning 3D point cloud representation often use a single dataset-specific training and testing approach, leading to performance drops due to significant domain shifts ...

Abstract: In the recent 3D object detection methods for point clouds, the combination of point-based methods and voxel-based methods is gradually becoming a trend. Point-based methods retain the ...

Abstract: We report on the first real-time operation of coherent point-to-multipoint in high-speed fiber-optic communications. The broadcast and aggregation network consists of a 400 Gb/s hub ...