3D Datasets

3D datasets are crucial for training and evaluating computer vision models that understand and interact with the three-dimensional world. Current research focuses on creating larger, more diverse datasets, addressing challenges like data scarcity and annotation costs through techniques such as synthetic data generation, weak supervision, and multi-modal data fusion (combining images, point clouds, and text). This includes developing novel architectures like Graph Neural Networks (GNNs) and Transformers for efficient processing and improved performance on tasks such as object detection, segmentation, and pose estimation. The resulting advancements have significant implications for various applications, including robotics, virtual reality, and augmented reality.