The disclosed subject matter is directed to employing machine learning models configured to predict 3D data from 2D images using deep learning techniques to derive 3D data for the 2D images. In some embodiments, a system is described comprising a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. The computer executable components comprise a reception component configured to receive two-dimensional images, and a three-dimensional data derivation component configured to employ one or more three-dimensional data from two-dimensional data (3D-from-2D) neural network models to derive three-dimensional data for the two-dimensional images.

The disclosed subject matter is directed to employing machine learning models configured to predict 3D data from 2D images using deep learning techniques to derive 3D data for the 2D images. In some embodiments, a system is described comprising a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. The computer executable components comprise a reception component configured to receive two-dimensional images, and a three-dimensional data derivation component configured to employ one or more three-dimensional data from two-dimensional data (3D-from-2D) neural network models to derive three-dimensional data for the two-dimensional images.

The disclosed subject matter is directed to employing machine learning models configured to predict 3D data from 2D images using deep learning techniques to derive 3D data for the 2D images. In some embodiments, a method is provided that comprises receiving, by a system comprising a processor, a panoramic image, and employing, by the system, a three-dimensional data from two-dimensional data (3D-from-2D) convolutional neural network model to derive three-dimensional data from the panoramic image, wherein the 3D-from-2D convolutional neural network model employs convolutional layers that wrap around the panoramic image as projected on a two-dimensional plane to facilitate deriving the three-dimensional data.

The disclosed subject matter is directed to employing machine learning models configured to predict 3D data from 2D images using deep learning techniques to derive 3D data for the 2D images. In some embodiments, a method is provided that comprises receiving, by a system comprising a processor, a panoramic image, and employing, by the system, a three-dimensional data from two-dimensional data (3D-from-2D) convolutional neural network model to derive three-dimensional data from the panoramic image, wherein the 3D-from-2D convolutional neural network model employs convolutional layers that wrap around the panoramic image as projected on a two-dimensional plane to facilitate deriving the three-dimensional data.

The disclosed subject matter is directed to employing machine learning models configured to predict 3D data from 2D images using deep learning techniques to derive 3D data for the 2D images. In some embodiments, a method is provided that comprises employing, by a system comprising a processor, one or more three-dimensional data from two-dimensional data (3D-from-2D) neural network models to derive three-dimensional data from one or more two-dimensional images captured of an object or environment from a current perspective of the object or environment viewed on or through a display of the device. The method further comprises, determining, by the system, a position for integrating a graphical data object on or within a representation of the object or environment viewed on or through the display based on the current perspective and the three-dimensional data.

The disclosed subject matter is directed to employing machine learning models configured to predict 3D data from 2D images using deep learning techniques to derive 3D data for the 2D images. In some embodiments, a method is provided that comprises employing, by a system comprising a processor, one or more three-dimensional data from two-dimensional data (3D-from-2D) neural network models to derive three-dimensional data from one or more two-dimensional images captured of an object or environment from a current perspective of the object or environment viewed on or through a display of the device. The method further comprises, determining, by the system, a position for integrating a graphical data object on or within a representation of the object or environment viewed on or through the display based on the current perspective and the three-dimensional data.

An image capture method performed by a depth sensor includes; emitting a first source signal having a first amplitude towards a scene, and thereafter emitting a second source signal having a second amplitude different from the first amplitude towards the scene, capturing a first image in response to the first source signal and capturing a second image in response to the second source signal, and interpolating the first and second images to generate a final image.

An image capture method performed by a depth sensor includes; emitting a first source signal having a first amplitude towards a scene, and thereafter emitting a second source signal having a second amplitude different from the first amplitude towards the scene, capturing a first image in response to the first source signal and capturing a second image in response to the second source signal, and interpolating the first and second images to generate a final image.

An encoding device evaluates a plurality of processing and/or post-processing algorithms and/or methods to be applied to a video stream, and signals a selected method, algorithm, class or category of methods/algorithms either in an encoded bitstream or as side information related to the encoded bitstream. A decoding device or post-processor utilizes the signaled algorithm or selects an algorithm/method based on the signaled method or algorithm. The selection is based, for example, on availability of the algorithm/method at the decoder/post-processor and/or cost of implementation. The video stream may comprise, for example, downsampled multiplexed stereoscopic images and the selected algorithm may include any of upconversion and/or error correction techniques that contribute to a restoration of the downsampled images.

A media data preparation device adapted to receive media data, including at least one processor, and at least one non-transitory memory having computer program code stored thereon for execution by the at least one processor, the computer program code including instructions to receive a set of metadata that is based on at least one spatial coordinate, where the set of metadata is associated with the media data, and determine a representation of the media data in a virtual reality space based on the set of metadata