3D Domain

3D domain research focuses on developing robust and efficient methods for processing and understanding three-dimensional point cloud data, primarily for tasks like semantic segmentation, instance segmentation, and object recognition. Current research emphasizes improving model efficiency (e.g., using state space models and avoiding quadratic complexity), leveraging self-supervised learning and multi-source pretraining to address data scarcity, and enhancing robustness to noise and domain shifts through techniques like reconstruction and simulation. These advancements are crucial for applications in autonomous driving, robotics, and augmented/virtual reality, where accurate and real-time 3D scene understanding is essential.