In one embodiment, a method includes accessing first image data generated by a first image sensor having a first filter array that has a first filter pattern. The first filter pattern includes a number of first filter types. The method also includes accessing second image data generated by a second image sensor having a second filter array that has a second filter pattern different from the first filter pattern. The second filter pattern includes a number of second filter types, the number of second filter types and the number of first filter types have at least one filter type in common. The method also includes determining a correspondence between one or more first pixels of the first image data and one or more second pixels of the second image data based on a portion of the first image data associated with the filter type in common.

There is provided an encoding apparatus, an encoding method, a decoding apparatus, and a decoding method that make it possible to acquire two-dimensional image data of a viewpoint corresponding to a predetermined display image generation method and depth image data without depending upon the viewpoint upon image pickup. A conversion unit generates, from three-dimensional data of an image pickup object, two-dimensional image data of a plurality of viewpoints corresponding to a predetermined display image generation method and depth image data indicative of a position of each of pixels in a depthwise direction of the image pickup object. An encoding unit encodes the two-dimensional image data and the depth image data generated by the conversion unit. A transmission unit transmits the two-dimensional image data and the depth image data encoded by the encoding unit. The present disclosure can be applied, for example, to an encoding apparatus and so forth.

There is provided an encoding apparatus, an encoding method, a decoding apparatus, and a decoding method that make it possible to acquire two-dimensional image data of a viewpoint corresponding to a predetermined display image generation method and depth image data without depending upon the viewpoint upon image pickup. A conversion unit generates, from three-dimensional data of an image pickup object, two-dimensional image data of a plurality of viewpoints corresponding to a predetermined display image generation method and depth image data indicative of a position of each of pixels in a depthwise direction of the image pickup object. An encoding unit encodes the two-dimensional image data and the depth image data generated by the conversion unit. A transmission unit transmits the two-dimensional image data and the depth image data encoded by the encoding unit. The present disclosure can be applied, for example, to an encoding apparatus and so forth.

A dynamic image virtualization system and method configured to utilize an AI model in order to conduct a reduced latency real-time prediction process upon at least one input image, wherein said prediction process is designated to create free-viewpoint 3D extrapolated output dynamic images tailored in advance to the preferences or needs of a user and comprising more visual data than the at least one input image.

An image-based method of modeling and rendering a three-dimensional model of an object is provided. The method comprises: obtaining a three-dimensional point cloud at each frame of a synchronized, multi-view video of an object, wherein the video comprises a plurality of frames; extracting a feature descriptor for each point in the point cloud for the plurality of frames without storing the feature descriptor for each frame; producing a two-dimensional feature map for a target camera; and using an anti-aliased convolutional neural network to decode the feature map into an image and a foreground mask.

An internal prediction method includes acquiring an image of a cell aggregate, calculating a feature amount related to a shape of the cell aggregate on the basis of the image, and outputting structure information related to an internal structure of the cell aggregate on the basis of the feature amount.

A method for positioning an internet of things (IoT) device with AR glasses is provided. The method includes: acquiring an indoor map; determining an AR-position of the AR glasses on the indoor map and a viewing direction of the AR glasses based on initial information of the AR glasses; receiving from the AR glasses tracking information concerning the IoT device to be positioned; and determining an IoT-position of the loT device to be positioned from the AR-position and the viewing direction in combination with the tracking information of the AR glasses.

A method for positioning an internet of things (IoT) device with AR glasses is provided. The method includes: acquiring an indoor map; determining an AR-position of the AR glasses on the indoor map and a viewing direction of the AR glasses based on initial information of the AR glasses; receiving from the AR glasses tracking information concerning the IoT device to be positioned; and determining an IoT-position of the loT device to be positioned from the AR-position and the viewing direction in combination with the tracking information of the AR glasses.

Provided is an image generation technology capable of suppressing unpleasantness associated with fluctuation in image quality caused by the viewer's viewpoint movement. A synthesis ratio determination unit of an image generation apparatus includes: an image quality evaluation index calculation unit that generates a synthesis image J.sub.1 from an observation viewpoint image I.sub.1 and an intermediate viewpoint image I.sub.2, and a synthesis image J.sub.3 from an observation viewpoint image I.sub.3 and an intermediate viewpoint image I.sub.2, for a plurality of synthesis ratios, calculates an image quality evaluation index in an observation viewpoint V.sub.1, an image quality evaluation index in an intermediate viewpoint V.sub.2, and an image quality evaluation index in an observation viewpoint V.sub.3 by using the synthesis images J.sub.1 and J.sub.3, and calculates a variation v of an image quality evaluation index by using the image quality evaluation index in the observation viewpoint V.sub.1, the image quality evaluation index in the intermediate viewpoint V.sub.2, and the image quality evaluation index in the observation viewpoint V.sub.3; and an image quality evaluation index comparison unit that determines a synthesis ratio A based on the variation v of the image quality evaluation index.

The present disclosure provides an apparatus and a method for selecting camera providing input images to synthesize virtual view images. According to the present disclosure, A method of selecting a camera providing as input image to synthesize a virtual view image, the method may comprise, for a camera providing an input image, determining whether or not the camera is comprised in a field of view (FoV) at a virtual view position and in response to the camera determined to be comprised in the field of view, selecting the camera to synthesize the virtual image, wherein the determining determines, by way of comparison, whether or not a direction from the virtual view position to a position of the camera is in the FoV at the virtual view position.