The present disclosure describes method, apparatus, and storage medium for identifying an identification code. The method includes obtaining, by a computer device, a to-be-detected picture. The computer device includes a memory storing instructions and a processor in communication with the memory. The method also includes detecting, by the computer device, an identification code in the to-be-detected picture to obtain a detection result, the detection result comprising target information of a target code corresponding to the identification code; sampling, by the computer device, the target code according to the target information, to obtain a sampled image; and decoding, by the computer device, the sampled image, to obtain an identification result corresponding to the identification code.

An information processing system is provided. A first generation unit generates a reduced image in which an original image is reduced. A display control unit causes a display unit of a predetermined apparatus to display the reduced image. A second generation unit generates, based on an input to the predetermined apparatus of a user, a mask image to be overlapped on the reduced image displayed on the display unit. An enlargement unit enlarges the mask image by using a different interpolation method in a case where the mask image has a predetermined property and in a case where the mask image does not have the predetermined property.

A system comprises an encoder configured to compress attribute information for a point cloud and/or a decoder configured to decompress compressed attribute information for the point cloud. Attribute values for at least one starting point are included in a compressed attribute information file and attribute correction values used to correct predicted attribute values are included in the compressed attribute information file. Attribute values are predicted based, at least in part, on attribute values of neighboring points and distances between a particular point for whom an attribute value is being predicted and the neighboring points. The predicted attribute values are compared to attribute values of a point cloud prior to compression to determine attribute correction values. A decoder follows a similar prediction process as an encoder and corrects predicted values using attribute correction values included in a compressed attribute information file.

Disclosed is an electronic apparatus. The electronic apparatus includes: a memory configured to store information regarding an artificial intelligence model including a plurality of layers; and a processor configured to perform interpolation processing on an input image and to process the interpolated image using the artificial intelligence model to obtain an output image, wherein the processor is configured to be operated in a first mode or a second mode based on an update of parameters used in at least one of the plurality of layers being required, the first mode including a mode in which the output image is obtained based on an image processed using the artificial intelligence model in which the parameters are updated and based on the interpolated image, and the second mode includes a mode in which the output image is obtained based on the interpolated image.

Disclosed are a video processing method and apparatus, and a device, a decoder, a system and a storage medium, applied to a video device. The method comprises: obtaining a video sequence of a first resolution, the video sequence comprising at least one video frame; and inputting the video sequence into a super-resolution network model to obtain a target video sequence of a second resolution, wherein the super-resolution network model at least comprises a first sub-network model and a second sub-network model, the first sub-network model is used for improving the resolution of the video sequence, and the second sub-network model is used for improving the quality of at least one image frame in the output result of the first sub-network model.

The present disclosure relates to a motion estimation method, a chip, an electronic device, and a storage medium. The present disclosure is beneficial to improving the accuracy of motion estimation.

Described herein is a technique for performing ray tracing operations. The technique includes encountering, at a non-leaf node, a pointer to a bottom-level acceleration structure having one or more delta instances; identifying an index associated with the pointer, wherein the index identifies an instance within the bottom-level acceleration structure; and obtaining data for the instance based on the pointer and the index.

The present application provides an image reconstruction method, a device, equipment, a system, and a computer-readable storage medium. Said method comprises: obtaining a target reconstruction model (S1); invoking a first convolutional layer in the obtained target reconstruction model to extract shallow layer features from the obtained image to be reconstructed (S2); invoking a residual network module in the target reconstruction model to obtain middle layer features from the shallow layer features (S3); invoking a densely connected network module in the target reconstruction model to obtain deep layer features from the middle layer features (S4); and invoking a second convolutional layer in the target reconstruction model to perform image reconstruction on the deep layer features so as to obtain a reconstructed image of the image to be reconstructed (S5). Said method improves the quality and resolution of a reconstructed image.

A system comprises an encoder configured to compress and encode data for a three-dimensional mesh using a video encoding technique. To compress the three-dimensional mesh, the encoder determines sub-meshes and for each sub-mesh: texture patches and geometry patches. Also the encoder determines patch connectivity information and patch texture coordinates for the texture patches and geometry patches. The texture patches and geometry patches are packed into video image frames and encoded using a video codec. Additionally, the encoder determines boundary stitching information for the sub-meshes. A decoder receives a bit stream as generated by the encoder and reconstructs the three-dimensional mesh.

An apparatus for estimating distance according to the present disclosure extracts feature maps from respective images including a reference image and a source image; projects a source feature map extracted from the source image onto hypothetical planes to generate a cost volume; sets sampling points on a ray extending from the viewpoint of the reference image in a direction corresponding to a target pixel in the reference image; interpolates features of the respective sampling points, using features associated with nearby coordinates; inputs the features corresponding to the respective sampling points into a classifier to calculate occupancy probabilities corresponding to the respective sampling points; and adds up products of the occupancy probabilities of the respective sampling points and the distances from the viewpoint of the reference image to the corresponding sampling points to estimate the distance from the viewpoint of the reference image to a surface of the object.