Depth Cue

Depth cueing, the use of depth information to improve computer vision tasks, is a rapidly advancing field focused on enhancing the accuracy and robustness of algorithms across various applications. Current research emphasizes integrating depth cues into existing models, such as UNets and other convolutional neural networks, often through novel modules that fuse depth and image data, or by employing depth-driven prompt learning. This work is significantly impacting fields like object detection, tracking, and semantic segmentation, particularly in challenging conditions like low light, haze, or crowded scenes, leading to more reliable and accurate results in autonomous driving, medical imaging, and robotics.