Unlabeled 3D

Unlabeled 3D data processing focuses on leveraging vast amounts of unlabeled three-dimensional data for various tasks, overcoming the limitations of expensive and time-consuming manual annotation. Current research emphasizes semi-supervised and weakly supervised learning techniques, employing methods like consistency regularization, pseudo-labeling, and spectral clustering to effectively utilize unlabeled information alongside limited labeled data. This work is crucial for advancing applications such as 3D object detection, segmentation, and vision-and-language navigation in robotics, autonomous driving, and medical imaging, where large-scale labeled datasets are often unavailable. Prominent approaches involve adapting existing deep learning architectures and incorporating novel algorithms like MAP-Elites and vision-language models to improve performance and efficiency.