3D Annotation

3D annotation focuses on automatically generating accurate three-dimensional labels for data, primarily to reduce the high cost and time associated with manual labeling. Current research emphasizes weakly supervised methods, often leveraging 2D annotations and incorporating techniques like proxy object injection, pseudo-labeling, and transformer-based architectures to infer 3D information. This work is crucial for advancing applications such as autonomous driving, robotics, and medical imaging, where large-scale, accurately labeled 3D datasets are essential for training robust and reliable models. The development of efficient and accurate 3D annotation techniques is therefore a significant driver of progress in these fields.