Abstract: The described aspects and implementations enable efficient object detection and tracking. In one implementation, disclosed is a method and a system to perform the method, the system including the sensing system configured to obtain sensing data characterizing an environment of the vehicle. The system further includes a data processing system operatively coupled to the sensing system and configured to process the sensing data using a first (second) set of neural network (NN) layers to obtain a first (second) set of features for a first (second) region of the environment, the first (second) set of features is associated with a first (second) spatial resolution. The data processing system is further to process the two sets of features using a second set of NN layers to detect a location of obj ect(s) in the environment of the vehicle and a state of motion of the object(s).

Abstract: A method includes obtaining, by a processing device, input data derived from a set of sensors associated with an autonomous vehicle (AV), extracting, by the processing device from the input data, a plurality of sets of features, generating, by the processing device using the plurality of sets of features, a fused bird's-eye view (BEV) grid. The fused BEV grid is generated based on a first BEV grid having a first scale and a second BEV grid having a second scale different from the first scale. The method further includes providing, by the processing device, the fused BEV grid for object detection.

Abstract: A system and method of deep learning using deep networks to predict new views from existing images may generate and improve models and representations from large-scale data. This system and method of deep learning may employ a deep architecture performing new view synthesis directly from pixels, trained from large numbers of posed image sets. A system employing this type of deep network may produce pixels of an unseen view based on pixels of neighboring views, lending itself to applications in graphics generation.

Abstract: A system and method of deep learning using deep networks to predict new views from existing images may generate and improve models and representations from large-scale data. This system and method of deep learning may employ a deep architecture performing new view synthesis directly from pixels, trained from large numbers of posed image sets. A system employing this type of deep network may produce pixels of an unseen view based on pixels of neighboring views, lending itself to applications in graphics generation.

Abstract: A method and apparatus for enabling virtual tags is described. The method may include receiving a first digital image data and virtual tag data to be associated with a real-world object in the first digital image data, wherein the first digital image data is captured by a first mobile device, and the virtual tag data includes metadata received from a user of the first mobile device. The method may also include generating a first digital signature from the first digital image data that describes the real-world object, and in response to the generation, inserting in substantially real-time the first digital signature into a searchable index of digital images. The method may also include storing, in a tag database, the virtual tag data and an association between the virtual tag data and the first digital signature inserted into the index of digital images.

Abstract: The present application discloses systems and methods for querying an image database. In one embodiment, a method is disclosed that includes maintaining in data storage a plurality of images and a plurality of geo-feature identifiers, where each image is associated with at least one geo-feature identifier that indicates at least one geographic cell in which the image was recorded and a visual feature of the image. The method further includes receiving a query image and an indication of a requested geographic location, determining at least one target geographic cell corresponding to the requested geographic location, and identifying at least one target visual feature within the query image. The method further includes determining at least one requested geo-feature identifier based on the at least one target geographic cell and the at least one target visual feature and selecting one or more images based on the at least one requested geo-feature identifier.

Abstract: A method and apparatus for enabling virtual tags is described. The method may include receiving a first digital image data and virtual tag data to be associated with a real-world object in the first digital image data, wherein the first digital image data is captured by a first mobile device, and the virtual tag data includes metadata received from a user of the first mobile device. The method may also include generating a first digital signature from the first digital image data that describes the real-world object, and in response to the generation, inserting in substantially real-time the first digital signature into a searchable index of digital images. The method may also include storing, in a tag database, the virtual tag data and an association between the virtual tag data and the first digital signature inserted into the index of digital images.

Abstract: A method and apparatus for enabling virtual tags is described. The method may include receiving a first digital image data and virtual tag data to be associated with a real-world object in the first digital image data, wherein the first digital image data is captured by a first mobile device, and the virtual tag data includes metadata received from a user of the first mobile device. The method may also include generating a first digital signature from the first digital image data that describes the real-world object, and in response to the generation, inserting in substantially real-time the first digital signature into a searchable index of digital images. The method may also include storing, in a tag database, the virtual tag data and an association between the virtual tag data and the first digital signature inserted into the index of digital images.

Abstract: A method and apparatus for enabling virtual tags is described. The method may include receiving a first digital image data and virtual tag data to be associated with a real-world object in the first digital image data, wherein the first digital image data is captured by a first mobile device, and the virtual tag data includes metadata received from a user of the first mobile device. The method may also include generating a first digital signature from the first digital image data that describes the real-world object, and in response to the generation, inserting in substantially real-time the first digital signature into a searchable index of digital images. The method may also include storing, in a tag database, the virtual tag data and an association between the virtual tag data and the first digital signature inserted into the index of digital images.