METHODS AND SYSTEMS FOR PREDICTING PRESSURE MAPS OF 3D OBJECTS FROM 2D PHOTOS USING DEEP LEARNING

Systems and methods for generating pressure maps of real-world objects using deep learning are disclosed. A structured 3D model of a real-world object is generated from a series of 2D photographs of the object, using a process which in some embodiments utilizes photogrammetry, a keypoint detection deep learning network (DLN), and retopology. In addition, object parameters of the object are received. A pressure map of the object is then generated by a pressure estimation deep learning network (DLN) based on the structured 3D model and the object parameters, where the pressure estimation DLN was trained on structured 3D models, object parameters, and pressure maps of a plurality of objects belonging to a given object category. The pressure map of the real-world object can be used in downstream processes, such as custom manufacturing.