Abstract: Systems and methods for predicting body part measurements of a user from depth images are disclosed. The method first receives a plurality of depth images of a body part of the user from an image-capturing device. Next, the method generates a plurality of individual point clouds based on the plurality of depth images. Next, the method stitches the plurality of individual point clouds into a stitched point cloud, and determines a measurement location based on the stitched point cloud. Finally, the method projects the measurement location to the stitched point cloud, and generates the body part measurement based on the projected measurement location. To determine the measurement location, one embodiment uses a morphed base 3D model, whereas another embodiment uses a 3D keypoint detection algorithm on the stitched point cloud. The method may be implemented on a mobile computing device with a depth sensor.

Abstract: A structured 3D model of a real-world object is generated from a series of 2D photographs of the object, using 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 DLN based on the structured 3D model and the object parameters. 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.