For information about point cloud data, a point cloud data sameness estimation apparatus and a point cloud data sameness estimation system in which accuracy of evaluating sameness is improved are provided. In the present disclosure, a point cloud data sameness estimation apparatus for estimating sameness of objects that are sources of two 3-dimensional point cloud datasets includes a point cloud data acquisition unit configured to acquire first point cloud data and second point cloud data including 3-dimensional point cloud data; a first neural network configured to output a first point cloud data feature, with information about the first point cloud data as an input into the first neural network; a second neural network configured to output a second point cloud data feature, with information about the second point cloud data as an input into the second neural network; and a sameness evaluation unit configured to output an evaluation about sameness of the first point cloud data and the second point cloud data, based on the first point cloud data feature and the second point cloud data feature, wherein a weight is mutually shared by the first neural network and the second neural network.

Provided is a method for predicting attribute information, a coder, a decoder, and a storage medium. The coder determines a current Morton code corresponding to a point to be predicted in a point cloud to be coded, determines a target Morton code corresponding to the point to be predicted based on the current Morton code and according to a preset neighbor information table, judges whether a neighbor point of the point to be predicted exists in the point cloud to be coded according to the target Morton code, and performs prediction to obtain a predicted attribute value of the point to be predicted according to attribute reconstruction information of the neighbor point in response to that the neighbor point exists in the point cloud to be coded.

The present disclosure provides a multi-view image decoding method including: obtaining, from a bitstream, three-dimensional geometry information indicating a three-dimensional space of a multi-view image, view independent component information indicating a view independent component, which is uniformly applied to every view, and view dependent component information indicating a view dependent component, which is differently applied according to views; determining a view dependent component for a texture map of a current view from the view dependent component information; generating the texture map of the current view from a view independent component of the view independent component information and the determined view dependent component for the current view; and reconstructing a current view image according to a three-dimensional space that is constructed according to the texture map of the current view and the three-dimensional geometry information.

A method for performing a computer-aided diagnosis (CAD) includes: acquiring a medical image set; generating a three-dimensional (3D) tumor distance map corresponding to the medical image set, each voxel of the tumor distance map representing a distance from the voxel to a nearest boundary of a primary tumor present in the medical image set; and performing neural-network processing of the medical image set to generate a predicted probability map to predict presence and locations of oncology significant lymph nodes (OSLNs) in the medical image set, wherein voxels in the medical image set are stratified and processed according to the tumor distance map.

A method for performing a computer-aided diagnosis (CAD) includes: acquiring a medical image set; generating a three-dimensional (3D) tumor distance map corresponding to the medical image set, each voxel of the tumor distance map representing a distance from the voxel to a nearest boundary of a primary tumor present in the medical image set; and performing neural-network processing of the medical image set to generate a predicted probability map to predict presence and locations of oncology significant lymph nodes (OSLNs) in the medical image set, wherein voxels in the medical image set are stratified and processed according to the tumor distance map.

A method for managing image data in an automotive lighting device. This method includes the steps of providing an image pattern, dividing the image pattern in rows or columns of pixels, and providing, for each row pattern, a plurality of linear segments. Also included is providing a breaking pixel, splitting a segment by the breaking pixel, compressing the data of the linear segments and sending the compressed data to the light module. The invention also provides an automotive lighting device for performing the steps of such a method.

A method for managing image data in an automotive lighting device. This method includes the steps of providing an image pattern, dividing the image pattern in rows or columns of pixels, and providing, for each row pattern, a plurality of linear segments. Also included is providing a breaking pixel, splitting a segment by the breaking pixel, compressing the data of the linear segments and sending the compressed data to the light module. The invention also provides an automotive lighting device for performing the steps of such a method.

An encoder may perform a dynamic encoding that adapts the encoding of hyperspectral data according to the number of bands of the electromagnetic spectrum that are captured by different imaging devices, the amount of data that is contained in each band, and/or encoding criteria that are specified by a user or that are automatically generated by the encoder for an optimal encoding of the hyperspectral data. The encoder may receive hyperspectral data for different electromagnetic spectrum bands. The encoder may determine an encoding resolution based on one or more of a number of bands and a maximum resolution within the received bands. The encoder may configure a block size for a file format that is used to store an encoding of the hyperspectral data based on the encoding resolution, and may encode the hyperspectral data contained within each band to at least one block of the block size.

An encoder may perform a dynamic encoding that adapts the encoding of hyperspectral data according to the number of bands of the electromagnetic spectrum that are captured by different imaging devices, the amount of data that is contained in each band, and/or encoding criteria that are specified by a user or that are automatically generated by the encoder for an optimal encoding of the hyperspectral data. The encoder may receive hyperspectral data for different electromagnetic spectrum bands. The encoder may determine an encoding resolution based on one or more of a number of bands and a maximum resolution within the received bands. The encoder may configure a block size for a file format that is used to store an encoding of the hyperspectral data based on the encoding resolution, and may encode the hyperspectral data contained within each band to at least one block of the block size.

Disclosed is a method and device for processing Image, and an image transmitting system. The method includes that: data to be transmitted are processed according to a first resolution to obtain first image data, wherein the image resolution represented by each row of image data in the first image data is the first resolution, and the first resolution is the maximum resolution set by a system; and the first image data is folded to obtain second image data, wherein the number of rows of the second image data is greater than that of the first image data, the image resolution represented by each row of image data in the second image data is a second resolution, and the second resolution is less than the first resolution.