Lidar Upsampling

Lidar upsampling aims to enhance the resolution of sparse LiDAR point cloud data, improving the detail and accuracy of 3D scene representations. Current research focuses on deep learning approaches, employing architectures like transformers and diffusion models, often leveraging intermediate range image representations to overcome the challenges posed by the irregular and sparse nature of LiDAR data. These advancements are crucial for improving the performance of autonomous vehicles and robotics applications that rely on accurate 3D perception, particularly where high-resolution LiDAR sensors are cost-prohibitive. The development of robust and efficient upsampling techniques is driving significant progress in the field.