Abstract: In a method for generating a multi-dimensional scene graph with a complex light field, entity features of entities are obtained by inputting respective 2-Dimensional (2D) images captured in multiple view directions into an object detection model, and features of a respective single-view-direction scene graph are obtained by predicting a semantic relation among the entities contained in a corresponding 2D image captured in each view direction. An entity correlation for an entity among the multiple view directions is determined as an entity re-identification result. A multi-dimensional bounding box for the entity is established based on the entity re-identification result and a geometric constraint of camera parameters. A feature fusion result is obtained by fusing features of respective single-view-direction scene graphs in the multiple view directions. The multi-dimensional scene graph with the complex light field is established.

Abstract: A method includes: acquiring a semantic primitive set of a multi-view image set; acquiring a coordinate offset by inputting coordinate information and a feature vector corresponding to a first grid sampling point of the semantic primitive set into a first network model, and acquiring a second grid of the semantic primitive set based on the coordinate offset and geometric attribute information of the semantic primitive set; acquiring first feature information of a second grid sampling point by inputting coordinate information and a feature vector corresponding to the second grid sampling point, and an observation angle value into a second network model, and acquiring second feature information of the semantic primitive set based on the first feature information; and acquiring a light field reconstruction result of the multi-view image set based on an observation angle value of the semantic primitive set and third feature information extracted from the second feature information.

Abstract: A method and a device for gigapixel-level light field intelligent reconstruction of a large-scale scene are provided. The method includes: obtaining a coarse three-dimensional geometric model based on a multi-view three-dimensional reconstruction system; constructing an implicit representation of the meta-deformed manifold on the coarse three-dimensional geometric model; and optimizing the implicit representation of the meta-deformed manifold to obtain the light field reconstruction in the form of free viewpoint rendering of the large-scale scene.

Abstract: A method for large scene elastic semantic representation and self-supervised light field reconstruction is provided. The method includes acquiring a first depth map set corresponding to a target scene, in which the first depth map set includes a first depth map corresponding to at least one 5 angle of view; inputting the first depth map set into a target elastic semantic reconstruction model to obtain a second depth map set, in which the second depth map set includes a second depth map corresponding to the at least one angle of view; and fusing the second depth map corresponding to the at least one angle of view to obtain a target scene point cloud corresponding to the target scene.

Abstract: Exemplary embodiments provide a system, a method, and computer-readable medium to facilitate a transaction at a shopping cart. A first image of a user's face is captured by a first camera at a device coupled to the shopping cart, and the first image is stored in a database. A second image of the user's face is captured by a second camera when one or more sensors at a fixture detect an item is being removed from the fixture. The second image is analyzed and it is determined that the face in the second image corresponds to the face in the first image. Product information corresponding to the item removed from the fixture is sent to the processing device associated with the user captured in the first and second images. The product information is stored at the processing device for completion of a transaction by the user.

Abstract: A retail shrinkage activity prediction and identification system that includes: a sensor control system, a first shrinkage database, a second shrinkage database, an analytics engine, and a machine learning engine. The sensor control system is communicatively coupled with a plurality of sensors arranged in a retail environment. The sensor control system is configured to control a setting of each of the plurality of sensors. The first shrinkage database includes retail shrinkage data for at least the retail environment. The retail shrinkage data includes at least one item at high risk for shrinkage or at least one time at high risk for shrinkage activity. The second shrinkage database includes external data related to shrinkage in a geographic area of the retail environment. The analytics engine is communicatively coupled with the first shrinkage database, the second shrinkage database, and the sensor control system.

Abstract: Systems, apparatuses, and methods are provided herein for facilitating group retail purchases via electronic user interfaces. A method comprises providing, with a control circuit, a group shopping interface to a plurality of user interface devices, receiving a purchase request from a member of a shopping group, the purchase request comprises a request to purchase one or more products, retrieving product characteristics associated with the one or more products from a product database, retrieving at least one purchase rule from a rules database storing purchase rules for a plurality of shopping groups, wherein the rules database comprises system populated purchase rules generated through a machine learning algorithm based on a group history of the shopping group, and automatically rejecting or automatically approving the purchase request based on the at least one purchase rule and the product characteristics associated with the one or more products.

Abstract: A retail shrinkage activity prediction and identification system that includes: a sensor control system, a first shrinkage database, a second shrinkage database, an analytics engine, and a machine learning engine. The sensor control system is communicatively coupled with a plurality of sensors arranged in a retail environment. The sensor control system is configured to control a setting of each of the plurality of sensors. The first shrinkage database includes retail shrinkage data for at least the retail environment. The retail shrinkage data includes at least one item at high risk for shrinkage or at least one time at high risk for shrinkage activity. The second shrinkage database includes external data related to shrinkage in a geographic area of the retail environment. The analytics engine is communicatively coupled with the first shrinkage database, the second shrinkage database, and the sensor control system.

Abstract: A method to measure strip shape in rolling and a method of using the measuring method to control the same are provided in present invention, which can dramatically simplify the computation during the measurement of strip shape. On the basis of a prior mathematical model, the invention is realized by applying a strip rigidity coefficient q reflecting the features of a piece to be rolled, and a shape rigidity coefficient m reflecting those of a mill, to express heredity coefficient .eta..