Lidar Point Cloud Compression

Lidar point cloud compression aims to reduce the massive data volume generated by lidar sensors, enabling efficient storage, transmission, and processing. Current research focuses on developing efficient compression algorithms, often employing deep learning models such as those based on range images, octrees, or attention mechanisms, to exploit spatial and temporal redundancies within the data. These advancements are crucial for various applications, including autonomous driving and robotics, where real-time processing of large lidar datasets is essential, and are driving improvements in both compression ratios and processing speeds. The development of fast, lossless and lossy compression techniques is a key area of ongoing investigation.