Abstract: A system is provided that stores a neural network model trained on a training dataset which indicates an association between first graphic information associated with one or more first objects and corresponding first plurality of depth images. The system receives second graphic information that corresponds to the one or more first objects. The system further applies the trained neural network model received on the second graphic information. The system predicts a first depth image from the first plurality of depth images based on the application of the trained neural network model on the received second graphic information. The system extracts first depth information from the predicted first depth image. The first depth information corresponds to the one or more first objects indicated by the second graphic information.

Abstract: A system for generation of training dataset is provided. The system controls a depth sensor to capture, from a first viewpoint, a first image a first depth value associated with the first object. The system receives tracking information from a handheld device associated with the depth sensor, based on a movement of the handheld device and the depth sensor in a 3D space. The system generates graphic information corresponding to the first object based on the received tracking information. The graphic information includes the first object from a second viewpoint. The system calculates a second depth value associated with the first object, based on the graphic information. The system generates, for a neural network model, a training dataset which includes a first combination of the first image and the first depth value, and a second combination of second images corresponding to the graphic information and the second depth value.

Abstract: A system for generation of training dataset is provided. The system controls a depth sensor to capture, from a first viewpoint, a first image a first depth value associated with the first object. The system receives tracking information from a handheld device associated with the depth sensor, based on a movement of the handheld device and the depth sensor in a 3D space. The system generates graphic information corresponding to the first object based on the received tracking information. The graphic information includes the first object from a second viewpoint. The system calculates a second depth value associated with the first object, based on the graphic information. The system generates, for a neural network model, a training dataset which includes a first combination of the first image and the first depth value, and a second combination of second images corresponding to the graphic information and the second depth value.

Abstract: A system is provided that stores a neural network model trained on a training dataset which indicates an association between first graphic information associated with one or more first objects and corresponding first plurality of depth images. The system receives second graphic information that corresponds to the one or more first objects. The system further applies the trained neural network model received on the second graphic information. The system predicts a first depth image from the first plurality of depth images based on the application of the trained neural network model on the received second graphic information. The system extracts first depth information from the predicted first depth image. The first depth information corresponds to the one or more first objects indicated by the second graphic information.

Abstract: Embodiments of systems and methods are disclosed for providing messages based on an identified ridership pattern of a user of a transit system. Embodiments can include receiving information associated with a plurality of transactions of the user of the transit system, and identifying a ridership pattern of the user of the transit system. A predicted time and duration that the user of the transit system will be at a predicted location can be determined based, at least in part, on the identified ridership pattern. A message can be formulated using this information, and the message can be sent to the user or other message subscriber. Messages can include a variety of information, including advertisements, transit status updates, and more.