LiDAR Completion

LiDAR completion aims to reconstruct complete 3D scenes from sparse LiDAR point cloud data, addressing the limitations of real-world LiDAR sensors which often miss data due to occlusion or sparsity. Current research heavily utilizes deep learning, focusing on diffusion models and convolutional neural networks to infer missing depth and semantic information, often incorporating temporal information or guidance from other sensors like cameras. These advancements are crucial for improving the perception capabilities of autonomous vehicles and other robotics applications that rely on accurate 3D scene understanding, leading to safer and more robust systems.