Implicit Neural Fusion

Implicit neural fusion (INF) techniques aim to combine information from multiple data sources, such as LiDAR and camera images or multispectral and hyperspectral imagery, within a unified neural representation. Current research focuses on developing efficient architectures, including those leveraging implicit neural fields and Fourier transforms, to address challenges like data misalignment and high-frequency information loss. These methods improve the accuracy and speed of tasks like image generation, sensor fusion in robotics, and hyperspectral image enhancement, offering significant advancements in various fields requiring multimodal data integration. The resulting improvements in data fusion efficiency and accuracy have broad implications for applications ranging from autonomous driving to remote sensing.