Three-dimensional object identification using two-dimensional image data

Abstract

Various approaches provide for visual similarity-based search techniques that allow a user to provide two-dimensional image data (e.g., still images or video) about an item and search for items having related three-dimensional features. In order to create an electronic catalog of items that is searchable by three-dimensional features, the features are extracted from image data for each item. The three-dimensional features can be extracted using a trained model, such as a trained neural network or other machine learning-based approach. Thus, three-dimensional features of items in the electronic catalog can be determined and used to select or rank the items in response to a visual search query based on determined three-dimensional features of the query item. For example, three-dimensional features of the query item can be used to query the electronic catalog of items, in which items having visual attributes of similar three-dimensional features are selected and returned as search results.