3D Learning

3D learning focuses on developing algorithms and models that can effectively process and understand three-dimensional data, aiming to improve the accuracy, efficiency, and robustness of applications dealing with real-world 3D environments. Current research emphasizes developing novel architectures like equivariant networks and masked autoencoders, alongside techniques for improving explainability and handling open-set scenarios, often leveraging pre-trained 2D models for knowledge transfer. These advancements are crucial for various fields, including robotics, autonomous driving, and computer vision, enabling more reliable and efficient interaction with the physical world.