3D Label

3D label generation focuses on automatically creating three-dimensional annotations for various data types, such as point clouds and volumetric images, reducing the need for expensive and time-consuming manual labeling. Current research emphasizes weakly supervised and self-supervised approaches, employing techniques like pseudo-labeling, domain adaptation, and multimodal data fusion (e.g., combining 2D image data with LiDAR or depth information) to generate accurate 3D labels from limited or no ground truth. This work is crucial for advancing applications like autonomous driving, medical image analysis, and robotics, where large-scale, accurately labeled 3D datasets are essential for training robust and reliable models.