Spatial Encoding

Spatial encoding focuses on representing spatial information, such as location, shape, and relationships between objects, in a format suitable for machine learning. Current research emphasizes developing robust and generalizable encoding methods, often employing deep neural networks like transformers and convolutional neural networks, along with techniques like Fourier transforms and manifold learning to capture complex spatial relationships from diverse data sources (e.g., images, sensor data, brain activity). These advancements are improving performance in various applications, including 3D shape analysis, autonomous driving, brain-computer interfaces, and image categorization, by enabling machines to better understand and reason about spatial information.