Annotation Free

Annotation-free methods aim to perform tasks like semantic segmentation and 3D scene understanding without relying on manually labeled training data, significantly reducing the cost and time associated with data annotation. Current research focuses on leveraging pre-trained vision-language models (like CLIP) and self-supervised learning techniques to generate pseudo-labels or transfer knowledge from readily available image datasets to 3D point cloud data. This approach is revolutionizing fields like autonomous driving and medical image analysis by enabling the development of accurate models even when labeled data is scarce or expensive to obtain.