Abstract: Various embodiments of the present invention relate generally to systems and methods for analyzing and manipulating images and video. According to particular embodiments, the spatial relationship between multiple images and video is analyzed together with location information data, for purposes of creating a representation referred to herein as a surround view for presentation on a device. An object included in the surround view may be manipulated along axes by manipulating the device along corresponding axes. In particular embodiments, a surround view can be separated into layers. Effects can be applied to one or more of these layers to enhance the interactive and immersive viewing experience of the surround view.

Abstract: Various embodiments of the present invention relate generally to systems and methods for analyzing and manipulating images and video. According to particular embodiments, the spatial relationship between multiple images and video is analyzed together with location information data, for purposes of creating a representation referred to herein as a surround view for presentation on a device. An object included in the surround view may be manipulated along axes by manipulating the device along corresponding axes. In particular embodiments, a surround view can be separated into layers. Effects can be applied to one or more of these layers to enhance the interactive and immersive viewing experience of the surround view.

Abstract: Various embodiments of the present invention relate generally to systems and methods for analyzing and manipulating images and video. According to particular embodiments, the spatial relationship between multiple images and video is analyzed together with location information data, for purposes of creating a representation referred to herein as a surround view. In particular embodiments, the surround view reduces redundancy in the image and location data, and presents a user with an interactive and immersive viewing experience.

Abstract: Various examples of the present disclosure include techniques and mechanisms for generating a surround view. According to various examples, a surround view is constructed from multiple images that are captured from different locations. A computer processor is used to create a three dimensional model that includes the content and context of the surround view. In some examples, the content and context can be segmented such that separate three dimensional models can be provided for each of the content of the surround view and the context of the surround view.

Abstract: According to various examples, a surround view can be used as a visual search query for an object to be searched and this surround view can be compared to a database of three dimensional models. A determination can then be made about whether any of the three dimensional models match the visual search query. Based on how closely the three dimensional models match the visual search query, matching objects can be provided in various formats such as ranked lists.

Abstract: Various embodiments of the present invention relate generally to systems and methods for analyzing and manipulating images and video. According to particular embodiments, the spatial relationship between multiple images and video is analyzed together with location information data, for purposes of creating a representation referred to herein as a surround view. In particular embodiments, a surround view can be generated by combining a panoramic view of an object with a panoramic view of a distant scene, such that the object panorama is placed in a foreground position relative to the distant scene panorama. Such combined panoramas can enhance the interactive and immersive viewing experience of the surround view.

Abstract: Learning image processing tasks from scene reconstructions is described where the tasks may include but are not limited to: image de-noising, image in-painting, optical flow detection, interest point detection. In various embodiments training data is generated from a 2 or higher dimensional reconstruction of a scene and from empirical images of the same scene. In an example a machine learning system learns at least one parameter of a function for performing the image processing task by using the training data. In an example, the machine learning system comprises a random decision forest. In an example, the scene reconstruction is obtained by moving an image capture apparatus in an environment where the image capture apparatus has an associated dense reconstruction and camera tracking system.

Abstract: Learning image processing tasks from scene reconstructions is described where the tasks may include but are not limited to: image de-noising, image in-painting, optical flow detection, interest point detection. In various embodiments training data is generated from a 2 or higher dimensional reconstruction of a scene and from empirical images of the same scene. In an example a machine learning system learns at least one parameter of a function for performing the image processing task by using the training data. In an example, the machine learning system comprises a random decision forest. In an example, the scene reconstruction is obtained by moving an image capture apparatus in an environment where the image capture apparatus has an associated dense reconstruction and camera tracking system.