3D Geometric Shape Search

In addition to traditional text-based searching technology, in 2006, Geolus Search had the ability to quickly locate parts from large heterogeneous data sources on the basis of geometric similarity. Traditional search engines rely on comparison and search capabilities based on part numbers and descriptions alone. While this method can be very useful, its limitations do not allow companies to fully optimize their parts management due to issues such as inconsistent standards and classifications, limited part number conventions and native language dependence. Geolus Search combined traditional search capabilities with revolutionary geometric search technology to accurately identify more duplicate parts and duplicate suspects. Its advanced geometry-based search capability promoted innovation through reuse and eliminates the time and expense associated with designing or purchasing duplicate parts.

Search based on geometric similarity, also known as shape-based search or geometric search, is a method used to find and retrieve objects or data that have a similar shape or geometrical properties to a given query object. This type of search is most often used in fields like 3D modeling, computer-aided design (CAD), image processing, and computer vision.

In the context of digital media, here's a basic description of how it works:

1. Query: A user provides a shape or an object, often by drawing or uploading a 2D or 3D model. For example, in a 3D model database, a user might upload a 3D model of a car part.
2. Feature Extraction: The system extracts geometric features from the query object and the objects in the database. These features can include various properties such as size, length, area, volume, curves, angles, or more complex shape descriptors.
3. Comparison: The system compares the geometric features of the query object with the geometric features of the objects in the database. This is usually done by calculating a similarity measure or distance between feature sets. The goal is to find objects in the database that have the smallest distance to the query object.
4. Results: The system returns the objects that have the highest geometric similarity to the query object. The results can often be ranked by their similarity score.

One of the challenges of geometric search is handling different scales, rotations, and translations of the objects. Various techniques and algorithms, such as scale-invariant feature transform (SIFT) for image processing, or the use of bounding boxes or normalizing in 3D modeling, can be used to overcome these challenges.

Moreover, the use of machine learning and artificial intelligence has greatly improved the efficiency and accuracy of geometric similarity search, allowing for more complex shape analysis and the ability to handle larger databases.