Depth Refinement

Depth refinement focuses on improving the accuracy and detail of depth maps, typically generated from low-resolution or noisy sources like monocular vision or sparse LiDAR data. Current research emphasizes leveraging various model architectures, including transformers and convolutional neural networks, often incorporating techniques like self-supervised learning, multi-view consistency checks, and edge-aware processing to enhance depth map quality. These advancements are crucial for applications requiring high-fidelity 3D information, such as autonomous driving, robotics, and 3D scene reconstruction, where accurate depth perception is paramount. The development of more efficient and robust depth refinement methods continues to be a significant area of focus.