Sparse Lidar

Sparse LiDAR, characterized by its limited point density, presents challenges for 3D scene understanding but offers advantages in cost and computational efficiency. Current research focuses on improving object detection and scene reconstruction from sparse LiDAR data, often integrating it with other sensor modalities like cameras, through techniques such as transformer networks, graph convolutional networks, and implicit scene representations. These advancements are crucial for applications like autonomous driving and robotics, enabling robust perception even with limited sensor data, and driving innovation in efficient 3D data processing.