RGB D Object

RGB-D object research focuses on leveraging both color (RGB) and depth (D) information from sensors to improve 3D object understanding and manipulation. Current research emphasizes self-supervised learning methods, aiming to train robust models without extensive labeled datasets, often employing convolutional neural networks (CNNs) and vision transformers (ViTs) for tasks like object recognition, pose estimation, and semantic segmentation. These advancements are significant for robotics, augmented reality, and other applications requiring accurate and efficient 3D scene interpretation, particularly in challenging real-world scenarios.