Abstract: Provided is a method for generating Augmented Reality (AR) content that includes: receiving a plurality of image frames of at least one scene captured by a plurality of participant devices in an AR or a Virtual Reality (VR) environment; storing the plurality of image frames and metadata associated with the plurality of image frames, in a database; receiving an AR content generation request to generate an AR content view of a user in the AR/VR environment, the AR content generation request including an identifier (ID) of the user and information of the at least one scene; retrieving a set of image frames from a plurality of stored image frames in the database based on the ID of the user, the information of the at least one scene, and metadata associated with the set of image frames, the set of images including the user in the at least one scene in the AR/VR environment; generating the AR content view of the user by combining the set of image frames retrieved from the database, based on the metadata associated

Abstract: A method for matching a visual source with a respective sound signal is provided. The method includes receiving a real-world sound input including a combination of one or more sound signals originating from a plurality of sound-generating objects, separating one or more sound signals from the real-world sound input, detecting sound generating objects included in the visual source, generating an association between each of the sound generating objects and the one or more separated sound signals, and matching, in real-time, each of the detected sound generating objects with respective sound signals from the one or more separated sound signals based on the association.

Abstract: There is provided a method for segmenting a point cloud by an electronic device. The method includes receiving the point cloud including colorless data and/or featureless data. Further, the method includes determining a normal vector for the received point cloud and/or a spatial feature for the received point cloud. Further, the method includes segmenting the point cloud based on the at least one of one or more normal vectors and one or more spatial features.

Abstract: A method for generating a three-dimensional (3D) model of an object includes receiving a two-dimensional (2D) view of at least one object as an input, measuring geometrical shape coordinates of the at least one object from the input, identifying texture parameters of the at least one object from the input, predicting geometrical shape coordinates and texture parameters of occluded portions of the at least one object in the 2D view by processing the measured geometrical shape coordinates of the at least one object, the identified texture parameters of the at least one object, and the occluded portions of the at least one object, and generating a 3D model of the at least one object by mapping the measured geometrical shape coordinates and the identified texture parameters to the predicted geometrical shape coordinates and the predicted texture parameters of the occluded portions of the at least one object.

Abstract: Elasticity of a particular product is calculated based on product demand against various price points. Accurate product demand calculations are ensured by calculating the price journey of the product, with appropriate adjustments made for out of stock conditions and promotions and or discounts. The price journey data is then input into an impact estimation algorithm which allows calculation of demand elasticity accounting for various real-world factors impacting demand and elasticity, such as: price of a substitute or compliment, competitor price, weather, local events, calendar events, and other factors. This approach allows for superior price journey-based item-specific elasticity calculation, which allows for superior optimization of item price to maximize volume and profit.

Abstract: A method for real-time semantic image segmentation using a monocular event-based sensor includes capturing a scene using a red, green, blue (RGB) sensor to obtain a plurality of RGB frames and an event sensor to obtain event data corresponding to each of the plurality of RGB frames, performing object labeling for objects in a first RGB frame among the plurality of RGB frames by identifying one or more object classes, obtaining an event velocity of the scene by fusing the event data corresponding to the first RGB frame and at least one subsequent RGB frame among the plurality of RGB frames, determining whether the event velocity is greater than a predefined threshold, and performing object labeling for objects in the at least one subsequent RGB frame based on the determination.

Abstract: A method for real-time semantic image segmentation using a monocular event-based sensor includes capturing a scene using a red, green, blue (RGB) sensor to obtain a plurality of RGB frames and an event sensor to obtain event data corresponding to each of the plurality of RGB frames, performing object labeling for objects in a first RGB frame among the plurality of RGB frames by identifying one or more object classes, obtaining an event velocity of the scene by fusing the event data corresponding to the first RGB frame and at least one subsequent RGB frame among the plurality of RGB frames, determining whether the event velocity is greater than a predefined threshold, and performing object labeling for objects in the at least one subsequent RGB frame based on the determination.

Abstract: Elasticity of a particular product is calculated based on product demand against various price points. Accurate product demand calculations are ensured by calculating the price journey of the product, with appropriate adjustments made for out of stock conditions and promotions and or discounts. The price journey data is then input into an impact estimation algorithm which allows calculation of demand elasticity accounting for various real-world factors impacting demand and elasticity, such as: price of a substitute or compliment, competitor price, weather, local events, calendar events, and other factors. This approach allows for superior price journey-based item-specific elasticity calculation, which allows for superior optimization of item price to maximize volume and profit.