Abstract: In order for the feature extractors to operate with sufficient accuracy, a high degree of training is required. In this situation, a neural network implementing the feature extractor may be trained by providing it with images having known correspondence. A 3D model of a city may be utilized in order to train a neural network for location detection. 3D models are sophisticated and allow manipulation of viewer perspective and ambient features such as day/night sky variations, weather variations, and occlusion placement. Various manipulations may be executed in order to generate vast numbers of image pairs having known correspondence despite having variations. These image pairs with known correspondence may be utilized to train the neural network to be able to generate feature maps from query images and identify correspondence between query image feature maps and reference feature maps. This training can be accomplished without requiring the capture of real images with known correspondence.

Abstract: An image abstraction engine is provided to characterize scenes like typically found in an urban setting. Specifically buildings and manmade structures have certain characteristic properties that may be abstracted and compressed in a manner that takes advantage of those characteristic properties. This allows for a more compact and computationally efficient abstraction and recognition.

Abstract: An image abstraction engine is provided to characterize scenes like typically found in an urban setting. Specifically buildings and manmade structures have certain characteristic properties that may be abstracted and compressed in a manner that takes advantage of those characteristic properties. This allows for a more compact and computationally efficient abstraction and recognition.