3D Transfer Learning

3D transfer learning aims to leverage knowledge learned from one 3D dataset to improve performance on a different, related task or dataset, addressing the challenge of limited labeled 3D data. Current research focuses on overcoming the limitations of existing methods, particularly in handling deformable shapes and bridging domain gaps between different 3D data modalities (e.g., LiDAR and camera data), often employing techniques like contrastive learning, masked image modeling, and neural rendering. These advancements are significant for improving the efficiency and generalizability of 3D perception systems in applications such as object detection, segmentation, and shape analysis, particularly in scenarios with scarce labeled data.