LiDAR Slice

LiDAR slice processing focuses on efficiently utilizing the partial point cloud data acquired from a LiDAR sensor's sequential scans, rather than waiting for the complete 360° scan. Current research emphasizes developing adaptive sampling strategies to optimize data acquisition and employing deep learning models, such as convolutional neural networks and transformers, to process these partial scans for tasks like 3D object detection and depth completion. This approach aims to reduce latency and improve the real-time performance of autonomous systems by enabling faster perception and decision-making, ultimately enhancing safety and efficiency.