Abstract: A computer-implemented method for automatically positioning an input 3D object representing a real object in an input 3D scene representing a room. The method includes obtaining a dataset having information about objects of a plurality of rooms. The method includes executing computer program instructions that cause attempting to identify first, second and/or third pairs. The method includes outputting one or more pairs among the set consisting of each identified pair and the counts of the one or more identified pairs. The method includes, for each outputted pair, determining a respective position of the input 3D object in the input 3D scene. The method includes positioning the input 3D object according to the respective position determined for one of the one or more outputted pairs. The method improves the positioning of an input 3D object in an input 3D scene.

Abstract: A computer-implemented method for generating a training dataset. The training dataset includes training patterns each including a 3D point cloud of a respective travelable environment. The generating method includes, for each 3D point cloud, obtaining a 3D surface representation of the respective travelable environment, determining a traveling path inside the respective travelable environment, and, generating a virtual scan of the respective travelable environment along the traveling path, thereby obtaining the 3D point cloud. Such a method forms an improved solution for generating a training dataset of 3D point clouds.

Abstract: It is proposed a method for factory operation. The method includes providing manufacturing tasks each represented by a law describing a manufacturing of a product by one or more manageable machines. The method includes obtaining one or more manufacturing constraints and one or more manufacturing events. The method includes determining an operating mode of the factory based on the one or more manufacturing constraints and on one or more product constraints. The determining includes one or more iterations. Each iteration computes a respective propensity of each manufacturing task representing a frequency with which the task is chosen for manufacturing the product given the constraints and/or a previous event(s) occurrence. The iteration ranks the tasks according to a propensity descending order. The iteration visits the tasks according to the ranking and affecting to each task one or more manageable machines and resources. The iteration executes the tasks until an event(s).

Abstract: A computer-implemented method for automatically furnishing a 3D room based on user preferences including obtaining at least one spatial relations graph of a virtual 3D room and a set of user preferences, converting the set of user preferences into a set of target parameters, computing, for each spatial relations graph: a set of Key Performance Indicator values, and a KPI distance, automatically selecting at least one most promising spatial relations graph, instantiating the most promising spatial relations graph into the 3D room to be furnished, displaying the furnished virtual 3D room proposal to the user, receiving an update of the user preferences, and reiterating until a stopping criterion is fulfilled.

Abstract: A computer-implemented method for automatically providing at least a template of a furnished virtual 3D room including 3D elements, including, for each furnished virtual 3D room: automatically extracting a spatial relations graph based on spatial relations between the 3D elements of the 3D room, said 3D elements including 3D architectural elements and 3D furnishing objects located in the furnished virtual 3D room, automatically extracting at least one zone from the 3D room based on the spatial relations graph, and extracting a set of constraints about a relative arrangement of said zone with respect to the room architecture or with respect to other zones of the 3D room, a zone being defined by a local spatial arrangement of at least one 3D furnishing object, and storing a template of the furnished virtual 3D room, said template including said zone and said set of constraints.

Abstract: It is proposed a method for factory operation. The method includes providing manufacturing tasks each represented by a law describing a manufacturing of a product by one or more manageable machines. The method includes obtaining one or more manufacturing constraints and one or more manufacturing events. The method includes determining an operating mode of the factory based on the one or more manufacturing constraints and on one or more product constraints. The determining includes one or more iterations. Each iteration computes a respective propensity of each manufacturing task representing a frequency with which the task is chosen for manufacturing the product given the constraints and/or a previous event(s) occurrence. The iteration ranks the tasks according to a propensity descending order. The iteration visits the tasks according to the ranking and affecting to each task one or more manageable machines and resources. The iteration executes the tasks until an event(s).