Large Scale Lidar

Large-scale LiDAR processing focuses on efficiently analyzing massive point cloud datasets for applications like autonomous driving and large-scale mapping. Current research emphasizes developing data-efficient methods, including semi-supervised and self-supervised learning techniques, to reduce the reliance on expensive manual annotation. This involves exploring novel model architectures and algorithms for tasks such as semantic segmentation, object detection, and scene flow estimation, often incorporating multi-modal data fusion (e.g., combining LiDAR with camera images). Advances in this field are crucial for improving the accuracy and scalability of 3D scene understanding in various applications, particularly those requiring real-time processing of large datasets.