3D Representation Learning

3D representation learning aims to create effective numerical descriptions of three-dimensional objects and scenes, enabling computers to understand and manipulate them. Current research heavily focuses on developing efficient and scalable architectures, such as transformers and autoencoders, often incorporating multi-scale processing and leveraging cross-modal information (e.g., combining 2D images with 3D point clouds). These advancements are crucial for improving performance in various downstream tasks, including object recognition, segmentation, and robotic manipulation, and are driving progress in fields like computer-aided diagnosis and autonomous systems.