Depth Feature

Depth features, representing three-dimensional spatial information, are increasingly crucial in computer vision tasks, aiming to improve accuracy and robustness by integrating geometric context with visual data. Current research focuses on effectively fusing depth features with image features using various architectures, including transformers, convolutional neural networks, and graph neural networks, often employing techniques like masked pre-training, attention mechanisms, and multi-scale processing to enhance performance. This integration is proving particularly valuable in applications such as semantic segmentation, object detection (especially camouflaged objects), and depth super-resolution, leading to state-of-the-art results in these fields. The ability to leverage depth information significantly improves the accuracy and reliability of computer vision systems across diverse applications.