Since there are sibling comments wondering what this means, here’s a perspective:
A database (simplest case is an array or a dict) isn’t fundamentally different from a function in its interface. To both of them you submit a query/argument and they return some value. A “function” might “compute” the value after the query is submitted, while the database might “store” a pre-computed value. Especially in physical contexts where there is a natural continuity in the thing being represented (think object density/radiance, as opposed to student names), it makes sense to also have the option to interpolate among records in a database.
Neural networks (being generic learnable function approximators) are a nice framework to straddle this boundary for generic cases.
In the specific example of the Nerf, we use a neural network rather than discrediting space and assigning a value per pixel/voxel. A consequent advantage is that we can have an a data description that is adaptive non-uniform in its information density, unlike a voxel grid.
There’s more to say, but I hope this gives a feel :-)
A shortcut to storing (and possibly also querying) data is to store (or "query" within) the function of that data? Seems to be in-line with what you wrote, and in essence I've always looked at neural networks as a kind of compression into complex functions of encoded data.
A database (simplest case is an array or a dict) isn’t fundamentally different from a function in its interface. To both of them you submit a query/argument and they return some value. A “function” might “compute” the value after the query is submitted, while the database might “store” a pre-computed value. Especially in physical contexts where there is a natural continuity in the thing being represented (think object density/radiance, as opposed to student names), it makes sense to also have the option to interpolate among records in a database.
Neural networks (being generic learnable function approximators) are a nice framework to straddle this boundary for generic cases.
In the specific example of the Nerf, we use a neural network rather than discrediting space and assigning a value per pixel/voxel. A consequent advantage is that we can have an a data description that is adaptive non-uniform in its information density, unlike a voxel grid.
There’s more to say, but I hope this gives a feel :-)