Example video processing systems and methods are described. In one implementation, compressed video data is received from a recording device. Additionally, metadata associated with the compressed video data is received such that the metadata includes frame-specific metadata associated with frames in the compressed video data. Further, an application program is received and configured to generate a real-time interactive experience for a user based on the compressed video data and the metadata associated with the compressed video data. A non-fungible token (NFT) is generated that includes the compressed video data, the metadata associated with the compressed video data, and the application program.

A method comprising: providing a 3D representation of at least one object as an input for an encoder (500); projecting the 3D representation onto at least one 2D patch (502); generating at least a geometry image and a texture image from the 2D patch (504); generating, based on the geometry image, a mesh comprising a number of vertices (506); mapping the number of vertices to two- dimensional (2D) coordinates of the texture image (508); and signalling said 2D coordinates of the texture image to be applied to the number of vertices of the mesh in or along a bitstream (510).

The present disclosure provides a method and apparatus for processing an image. A specific implementation includes: acquiring a top view of a road; identifying a position of a lane line from the top view; cutting the top view into at least two areas, and determining, according to the position of the lane line in each area, a width of a lane in the each area and an average width of the lane in the top view; calculating a first perspective correction matrix by optimizing a first loss function, the first loss function being used to represent a difference between the width of the lane in the each area and the average width of the lane in the top view; and performing a lateral correction on the top view through the first perspective correction matrix to obtain a first corrected image.

One embodiment provides a computer-implemented method that includes providing a dynamic list structure that stores one or more detected object bounding boxes. Temporal analysis is applied that updates the dynamic list structure with object validation to reduce temporal artifacts. A two-dimensional (2D) buffer is utilized to store a luminance reduction ratio of a whole video frame. The luminance reduction ratio is applied to each pixel in the whole video frame based on the 2D buffer. One or more spatial smoothing filters are applied to the 2D buffer to reduce a likelihood of one or more spatial artifacts occurring in a luminance reduced region.

The present disclosure provides an electronic apparatus capable of capturing an image without being affected by an abnormality on a display surface.

The electronic apparatus includes: a display unit; an imaging unit that is disposed on an opposite side to a display surface of the display unit; an abnormality detection unit that detects an abnormality on the display surface; and a display control unit that highlights a position where the abnormality detected by the abnormality detection unit occurs on the display unit.

A method, performed by an augmented reality (AR) device including a vision correction lens, of detecting a gaze of a user is provided. The method includes obtaining lens characteristic information about the vision correction lens arranged to overlap a light guide plate in a gaze direction of the user, emitting light for gaze tracking toward a light reflector through a light emitter, wherein the emitted light is reflected by the light reflector and then directed to an eye of the user, receiving a light reflected by the eye of the user through a light receiver, obtaining an eye image of the user based on the light received, adjusting the eye image of the user based on the lens characteristic information about the vision correction lens, and obtaining gaze information based on the adjusted eye image.

Embodiments of the present disclosure provide a method and apparatus for processing a video frame, and relates to the field of computer vision technology. The method may include: acquiring a plurality of candidate first-order radial distortion parameters preset for a to-be-processed video frame, and acquiring a specified value of a specified radial distortion parameter; performing radial distortion correction on the to-be-processed video frame to obtain a first initial corrected video frame; selecting a first initial corrected video frame in which a local region except for a center region after distortion correction includes a largest number of straight line segments; and determining a candidate first-order radial distortion parameter corresponding to the selected first initial corrected video frame for use as a target first-order radial distortion parameter of the to-be-processed video frame.

A method for point cloud decoding includes receiving a bitstream. The method also includes decoding the bitstream into multiple frames that include pixels. Certain pixels of the multiple frames correspond to points of a three-dimensional (3D) point cloud. The multiple frames include a first set of frames that represent locations of the points of the 3D point cloud and a second set of frames that represent attribute information for the points of the 3D point cloud. The method further includes reconstructing the 3D point cloud based on the first set of frames. Additionally, the method includes identifying a first portion of the points of the reconstructed 3D point cloud based at least in part on a property associated with the multiple frames. The method also includes modifying a portion of the attribute information. The portion of the attribute information that is modified corresponds to the first portion of the points.

One variation of a method for automatically generating a planogram for a store includes: dispatching a robotic system to autonomously navigate within the store during a mapping routine; accessing a floor map of the floor space generated by the robotic system from map data collected during the mapping routine; identifying a shelving structure within the map of the floor space; defining a first set of waypoints along an aisle facing the shelving structure; dispatching the robotic system to navigate to and to capture optical data at the set of waypoints during an imaging routine; receiving a set of images generated from optical data recorded by the robotic system during the imaging routine; identifying products and positions of products in the set of images; and generating a planogram of the shelving segment based on products and positions of products identified in the set of images.

Disclosed is a distortion rectification method, comprising: taking a wide-angle photograph using a camera of a terminal; determining distortion regions and non-distortion regions in the wide-angle photograph; obtaining a target distortion region selected by a user; dividing the target distortion region into M grid regions of a first pre-set size, wherein M is an integer greater than or equal to one; and respectively performing distortion rectification on the M grid regions of the first pre-set size. Also disclosed is a terminal.