Neural Fields across Fields: Methods and Applications of Implicit Neural Representations

Addressing problems in different science and engineering disciplines often requires solving optimization problems, including via machine learning from large training data. One class of methods has recently gained significant attention for problems in computer vision and visual computing: coordinate-based neural networks parameterizing a field, such as a neural network that maps a 3D spatial coordinate to a flow field in fluid dynamics, or a colour and density field in 3D scene representation. Such networks are often referred to as "neural fields". The application of neural fields in visual computing has led to remarkable progress on various computer vision problems such as 3D scene reconstruction and generative modelling, leading to more accurate, higher fidelity, more expressive, and computationally cheaper solutions. Given that neural fields can represent spatio-temporal signals in arbitrary input/output dimensions, they are highly general as a tool to reason about real-world observations, be it common modalities in machine learning and vision such as image, 3D shapes, 3D scenes, video, speech/audio or more specialized modalities such as flow fields in physics, scenes in robotics, medical images in computational biology, weather data in climate science. However, though some adjacent fields such as robotics have recently seen an increased interest in this area, most of the current research is still confined to visual computing, and the application of neural fields in other fields is in its early stages. We thus propose a workshop that aims to bring together researchers from a diverse set of backgrounds including machine learning, computer vision, robotics, applied mathematics, physics, chemistry, biology and climate science to exchange ideas and expand the domains of application of neural fields.