  Neural fields are evolving towards a general-purpose continuous
representation for visual computing. Yet, despite their numerous appealing
properties, they are hardly amenable to signal processing. As a remedy, we
present a method to perform general continuous convolutions with general
continuous signals such as neural fields. Observing that piecewise polynomial
kernels reduce to a sparse set of Dirac deltas after repeated differentiation,
we leverage convolution identities and train a repeated integral field to
efficiently execute large-scale convolutions. We demonstrate our approach on a
variety of data modalities and spatially-varying kernels.
