Unsupervised Depth Completion

Unsupervised depth completion aims to reconstruct a full, dense depth map from sparse depth measurements, typically obtained from sensors like LiDAR, without relying on ground truth depth data for training. Recent research focuses on improving the robustness and accuracy of these methods by addressing limitations in data augmentation techniques, exploring active data collection strategies for more informative training datasets, and developing novel network architectures that leverage both relative and absolute depth information, often incorporating structural regularities from visual SLAM. These advancements are crucial for improving the performance of robotics and autonomous systems that rely on accurate depth perception, particularly in challenging environments with limited or noisy sensor data.