# Graphics ### Natural world * LOD procedural generation: generate rough large-scale statistics like orbit, size, location, color + normal map, & store this only long-term * Then generate more and more details with a neural network client-side, on the fly * Will probably train a specialized neural network that given high-level stats & view, generates details that look identical to the high-level picture when zoomed out. * Can train this with sth like a GAN, with training samples produced "in reverse" by running a slow algorithm that procedurally generates detailed planets, then zoom out * No bikes or cars, or in general land vehicles: physics simulation too computationally intensive. Also who would want cars in a world where you can have hovercars ### Notes Need some fast GI approximation that doesn't rely on anything prebaked Target: half life 2 quality on integrated graphics Z-buffer tricks in space, possibly quadruple-precision floats in galaxy space as ground truth? Depends on travel mechanics --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://ostomo.gitbook.io/ostomo-notes/graphics.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.