By tokenizing 3D shapes based on semantic importance rather than spatial detail levels, you can train autoregressive 3D generation models that are 10-1000x more token-efficient while maintaining or improving quality.
LoST is a new way to break down 3D shapes into tokens (small pieces) for AI models to process. Instead of using spatial hierarchies like existing methods, it orders tokens by semantic importance—so early tokens capture the main shape, and later tokens add fine details. This makes 3D generation models much more efficient, using 90-99% fewer tokens than previous approaches.