3D Object Recognition

3D object recognition aims to enable computers to identify and understand three-dimensional objects from various data sources, such as point clouds, RGB-D images, and multiple camera views. Current research emphasizes improving robustness to noise and variations in viewpoint, often employing transformer-based architectures, multi-modal fusion techniques (combining image and point cloud data), and self-supervised or few-shot learning methods to address data scarcity. These advancements are crucial for applications in robotics, autonomous driving, and augmented reality, where accurate and reliable 3D object understanding is essential.