  In the realm of computer vision, Neural Fields have gained prominence as a
contemporary tool harnessing neural networks for signal representation. Despite
the remarkable progress in adapting these networks to solve a variety of
problems, the field still lacks a comprehensive theoretical framework. This
article aims to address this gap by delving into the intricate interplay
between initialization and activation, providing a foundational basis for the
robust optimization of Neural Fields. Our theoretical insights reveal a
deep-seated connection among network initialization, architectural choices, and
the optimization process, emphasizing the need for a holistic approach when
designing cutting-edge Neural Fields.
