RGB D Salient Object Detection

RGB-D salient object detection aims to identify visually prominent regions in images by combining color (RGB) and depth information. Current research emphasizes effective fusion of these modalities, often employing transformer-based architectures or convolutional neural networks (CNNs) with attention mechanisms to capture both local and global context, and address challenges like depth map inconsistencies. These advancements improve accuracy and efficiency in identifying salient objects, impacting applications such as robotics, autonomous driving, and image editing. The field is also exploring unsupervised and semi-supervised learning techniques to reduce reliance on large, manually labeled datasets.