An apparatus comprises a store (209) storing a set of anchor poses for a scene, as well as typically 3D image data for the scene. A receiver (201) receives viewer poses for a viewer and a render pose processor (203) determines a render pose in the scene for a current viewer pose of the viewer pose where the render pose is determined relative to a reference anchor pose. A retriever (207) retrieves 3D image data for the reference anchor pose and a synthesizer (205) synthesizes images for the render pose in response to the 3D image data. A selector selects the reference anchor pose from the set of anchor poses and is arranged to switch the reference anchor pose from a first anchor pose of the set of anchor poses to a second anchor pose of the set of anchor poses in response to the viewer poses.

An imagery processing system determines pixel color values for pixels of captured imagery from volumetric data, providing alternative pixel color values. A main imagery capture device, such as a camera, captures main imagery such as still images and/or video sequences, of a live action scene. Alternative devices capture imagery of the live action scene, in some spectra and form, and capture information related to pixel color values for multiple depths of a scene, which can be processed to provide reconstruction.

Apparatuses, systems, and techniques are presented to perform monocular view synthesis of a dynamic scene. Single and multi-view depth information can be determined for a collection of images of a dynamic scene, and a blender network can be used to combine image features for foreground, background, and missing image regions using fused depth maps inferred form the single and multi-view depth information.

Techniques for efficiently generating and displaying light-field data are disclosed. In one particular embodiment, the techniques may be realized as a method for generating light-field data, the method comprising receiving input image data, synthesizing a first plurality of viewpoints based on the input image data, synthesizing a second plurality of viewpoints based on cached image data, combining the first and second plurality of viewpoints, yielding a plurality of blended viewpoints, displaying the plurality of blended viewpoints, and caching image data associated with the plurality of blended viewpoints.

Methods to solve the problem of performing fusion of images acquired with two cameras with different type sensors, for example a visible (VIS) digital camera and an short wave infrared (SWIR) camera, include performing image space equalization on images acquired with the different type sensors before performing rectification and registration of such images in a fusion process.

A multi-camera zoom control method and apparatus, and an electronic system and a storage medium. The method includes: in the process of a first camera collecting an image, if the current set magnification input by a user is in a magnification transition zone, starting a second camera; acquiring a corresponding stereo correction matrix on the basis of calibration parameters of the first camera and the second camera; calculating a translation matrix on the basis of an acquired pixel position corresponding relationship between the same content regions of interest that correspond to a first zoomed image and a second zoomed image and in combination with the current set magnification, and then calculating a smooth transition transformation matrix in combination with the stereo correction matrix; and performing, by applying the smooth transition transformation matrix, affine transformation on an image output by the first camera, to obtain a display image of a device.

An image processing method includes a first step of acquiring a first image having disparity information and refocus information, and a second step of inputting the first image or the disparity information and the refocus information into a machine learning model, and of generating a second image having an in-focus position different from an in-focus position of the first image based on the refocus information. The refocus information is information on a distance between the in-focus position of the first image and the in-focus position of the second image.

Embodiments of systems and methods for multi-aperture ranging are disclosed. An embodiment of an image processing system includes at least one processor and memory configured to receive a multi-aperture image set that includes a high-resolution subaperture image and a low-resolution subaperture image, wherein the high-resolution subaperture image and the low-resolution subaperture image were captured simultaneously from a camera using dissimilar focal lengths, predict a high-resolution predicted disparity map from the high-resolution subaperture image using a neural network, predict a low-resolution predicted disparity map from the low-resolution subaperture image using the neural network, and generate an integrated range map from the high-resolution and low-resolution predicted disparity maps, wherein the integrated range map includes an array of range information that corresponds to the multi-aperture image set and that is generated by overlaying common points in both the high-resolution predicted disparity map and the low-resolution predicted disparity map.

Disclosed is a cross-view image optimizing method and apparatus, and a computer equipment and a readable storage medium. The method includes: acquiring a sample image and a pre-trained cross-view image generating model; generating an multi-dimensional cross-view image of the sample image by a multi-dimensional feature extracting module of the first generator to obtain dimension features and cross-view initial images at multiple dimensions; obtaining a multi-dimensional feature map with corresponding dimension features by the second generator; inputting the multi-dimensional feature map to a multi-channel attention module of the second generator for feature extraction and calculating a feature weight of each attention channel, obtaining attention feature images, attention images and feature weights in a preset number of the attention channels; and weighting and summing the attention images and the attention feature images of all the channels according to the feature weights, and obtaining a cross-view target image.

Disclosed herein is an immersive video processing method. The immersive video processing method includes: determining a priority order of pruning for source videos; extracting patches from the source videos based on the priority order of pruning; generating at least one atlas based on the extracted patches; and encoding metadata. Herein, a first flag indicating whether or not an atlas includes a patch including information on an entire region of a first source video may be encoded into the metadata.