A system allows a user to create new designs for apparel and preview these designs before manufacture. Software and lasers are used in finishing apparel to produce a desired wear pattern or other design. The user's preview may be based upon a two-dimensional image of a wear pattern in a laser input file and, from a set of two-dimensional images of a base garment, create a three-dimensional view of the base garment with the wear pattern.

A computer system maintains structure data indicating geometrical constraints for each structure category of a plurality of structure categories. The computer system generates a virtual 3D representation of a structure based on a set of images depicting the structure. For each image in the set of images, one or more landmarks are identified. Based on the landmarks, a candidate structure category is selected. The virtual 3D representation is generated based on the geometrical constraints of the candidate structure category and the landmarks identified in the set of images.

A space layout system performs various automated layout techniques to generate space layouts for office and other workspaces. The system can access building information modeling (BIM) information fora space, and automatically determine a layout of desks or other furniture for the space. The system can also provide to receive user input and automatically modify or reconfigure the layout of the space using the automated techniques.

A space layout system performs various automated layout techniques to generate space layouts for office and other workspaces. The system can access building information modeling (BIM) information fora space, and automatically determine a layout of desks or other furniture for the space. The system can also provide to receive user input and automatically modify or reconfigure the layout of the space using the automated techniques.

Techniques for creating a design for a physical device are disclosed. A computing system receives a design specification. The design specification includes a design region, one or more ports, and a port location perimeter. The computing system determines an initial proposed design based on the design specification that includes the design region and a location for each port of the one or more ports within the port location perimeter. The computing system optimizes the design region of the initial proposed design to create an improved design region, and optimizes at least one location of a port of the one or more ports within the port location perimeter to create an improved proposed design.

Techniques for creating a design for a physical device are disclosed. A computing system receives a design specification. The design specification includes a design region, one or more ports, and a port location perimeter. The computing system determines an initial proposed design based on the design specification that includes the design region and a location for each port of the one or more ports within the port location perimeter. The computing system optimizes the design region of the initial proposed design to create an improved design region, and optimizes at least one location of a port of the one or more ports within the port location perimeter to create an improved proposed design.

A learning dataset generation device includes: a memory that stores three-dimensional CAD data of a workpiece and a container; and one or more processors including hardware, wherein the one or more processors are configured to use the three-dimensional CAD data of the workpiece and the container, stored in the memory, to generate, in a three-dimensional virtual space, a plurality of imaging objects in which a plurality of the workpieces are bulk-loaded in different forms inside the container, acquire a plurality of virtual distance images by measuring each of the generated imaging objects by means of a virtual three-dimensional measurement machine disposed in the three-dimensional virtual space, accept at least one teaching position for each of the acquired virtual distance images, and generate a learning dataset by associating the accepted teaching position with each of the virtual distance images.

A learning dataset generation device includes: a memory that stores three-dimensional CAD data of a workpiece and a container; and one or more processors including hardware, wherein the one or more processors are configured to use the three-dimensional CAD data of the workpiece and the container, stored in the memory, to generate, in a three-dimensional virtual space, a plurality of imaging objects in which a plurality of the workpieces are bulk-loaded in different forms inside the container, acquire a plurality of virtual distance images by measuring each of the generated imaging objects by means of a virtual three-dimensional measurement machine disposed in the three-dimensional virtual space, accept at least one teaching position for each of the acquired virtual distance images, and generate a learning dataset by associating the accepted teaching position with each of the virtual distance images.

A computer-implemented method of machine-learning is described that includes obtaining a dataset of virtual scenes. The dataset of virtual scenes belongs to a first domain. The method further includes obtaining a test dataset of real scenes. The test dataset belongs to a second domain. The method further includes determining a third domain. The third domain is closer to the second domain than the first domain in terms of data distributions. The method further includes learning a domain-adaptive neural network based on the third domain. The domain-adaptive neural network is a neural network configured for inference of spatially reconfigurable objects in a real scene. Such a method constitutes an improved method of machine learning with a dataset of scenes including spatially reconfigurable objects.

A computer-implemented method of machine-learning is described that includes obtaining a dataset of virtual scenes. The dataset of virtual scenes belongs to a first domain. The method further includes obtaining a test dataset of real scenes. The test dataset belongs to a second domain. The method further includes determining a third domain. The third domain is closer to the second domain than the first domain in terms of data distributions. The method further includes learning a domain-adaptive neural network based on the third domain. The domain-adaptive neural network is a neural network configured for inference of spatially reconfigurable objects in a real scene. Such a method constitutes an improved method of machine learning with a dataset of scenes including spatially reconfigurable objects.