structuresfrom

Structuresfrom is a term used in computing to denote a function or process that derives one or more structural representations from input data, templates, or constraints. It is used in areas such as computer graphics, data modeling, and visualization, with the aim of turning raw information into organized, navigable structures.

Typically, a structuresfrom workflow takes an input source—such as geometric points, text, sensor streams, or templates—and an options object that specifies extraction rules, constraints, or learned models. The process may combine clustering, graph construction, rule-based inference, optimization, and machine learning to generate outputs. The result is a set of structures that can be explored, analyzed, or rendered.

Outputs are usually a set of structures (for example graphs, trees, meshes, or scene graphs) plus metadata about type, confidence, and provenance. Outputs can be deterministic or probabilistic, allowing multiple plausible structures for ambiguous data. The approach is often implemented as a library function or API call, such as structuresFrom(input, options), returning one or more structures with associated provenance information.

Examples include deriving a wireframe graph and a surface mesh from a 3D point cloud; constructing a dependency graph from a corpus of text; or instantiating a structural model from schematic templates. The method is adaptable across domains, supporting various data modalities and modeling goals.

Limitations include dependence on input quality and chosen models, sensitivity to noise and missing data, and computational costs that grow with data size and model complexity.

See also: Structure from motion, Graph construction, Data modeling, Schema inference.