Pointint

Pointint is a concept in computational geometry and data indexing that refers to a compact, scalar code assigned to a point in a space. The Pointint value is produced by a configurable scoring function that blends coordinates, dimension weights, and a reference origin. In typical use, the function is deterministic, so identical inputs yield identical codes, enabling reproducible indexing.

Pointint codes are designed to be locality-preserving, meaning points that are close in the chosen metric tend to receive similar codes. This property supports approximate nearest-neighbor search and range queries by operating on codes rather than full coordinate data, which can reduce memory usage and improve query performance in large datasets.

Implementation often resembles other space-partitioning and hashing approaches. Variants may include grid-based Pointint, angle-based Pointint, or hybrid schemes. Practitioners choose a metric (Euclidean, Manhattan, etc.), discretization granularity, and weighting to balance accuracy and coding density. Careful handling of boundaries and scale is needed to avoid excessive collisions or code inflation.

Applications include spatial databases, geographic information systems, computer vision, and pattern recognition, where rapid similarity checks are valuable. Limitations include the potential for collisions in dense regions and the lack of a universal standard, as different implementations optimize for different trade-offs.

Origin and terminology are recent; Pointint appears in ongoing discussions of indexing and hashing strategies. It is not an established standard, and published definitions vary across sources.