In Artificial Intelligence (AI) research, space is often implicitly assumed as a prerequisite for perception; specifically, objective three-dimensional space is treated as a frame within which objects reside, and perception is conceived as the process of constructing internal representations that mirror external objects. While this assumption has proven effective in certain applications, we argue that it is inadequate for Artificial General Intelligence (AGI). Through exploring the essence of spatial sense and objects, we propose a normative theory that aims to enable AGI systems to understand space and objects without assuming a predetermined-dimensional space or external objects. Several illustrative examples are provided to support the theory. Nonetheless, the question of how to build a complete perception system grounded in this theory remains open, with the primary challenge being the design of efficient learning mechanisms. Despite this, the present work may offer a theoretical foundation for the development of embodied AGI systems capable of acquiring understandings of space and objects.