Abstract: A decoding method is a decoding method performed by a decoding device, the decoding method including steps of: acquiring, from among a plurality of slice numbers indicating a plurality of slices associated with encoded data of the entire point cloud distributed in a three-dimensional space, a first slice number indicating a first slice associated with encoded data of a point cloud of a first space that is a portion of the three-dimensional space, on the basis of a pointer indicating the first slice; and acquiring the encoded data of the point cloud of the first space on the basis of the first slice number, and decoding point cloud data of the first space from the encoded data of the point cloud of the first space.

Abstract: An encoding device includes: an association processing unit that associates first encoded data which is encoded data of an original image with second encoded data which is encoded data of a decoded image which is a result of decoding of the encoded data of the original image; and an encoding unit that encodes, based on the result of associating the first encoded data with the second encoded data, a target image which is an image to be encoded.

Abstract: A coding apparatus is a coding apparatus for coding an original image and includes: a division section that divides the original image into blocks that are a plurality of areas to acquire the plurality of blocks; a determination section that, for each of the blocks, determines whether or not to determine the block as an interpolation target; and a substitution section that substitutes a value of a pixel included in the block determined as the interpolation target, with a value that decreases a code amount of the block determined as the interpolation target. The determination section determines whether or not to determine the area that is a target of the determination as an area that is the interpolation target, using an evaluation based on an accuracy of prediction of an image of the block by intra prediction or inter prediction and a degree of the area that is the interpolation target being a generated one.

Abstract: An encoding device includes a division unit that acquires three-dimensional data representing positions of a plurality of points distributed along a surface of an object in a three-dimensional space and divides a parent space including the points in the three-dimensional space into a plurality of child spaces and an encoding unit that changes, based on a position of a target space, which is the child space, to which a sign representing whether the points are included is allocated, according to whether the points are included in a first child space adjacent to the target space, processing for allocating the sign to the target space and a second child space adjacent to the target space.

Abstract: A point cloud data processing device includes: a point cloud data storage unit that stores a plurality of pieces of point cloud data measured at an arbitrary measurement time at a plurality of measurement positions in a three-dimensional space; and a merged point cloud data generation unit that generates merged point cloud data in a time-series order based on the point cloud data stored in the point cloud data storage unit, the merged point cloud data including the point cloud data corresponding to any one of the measurement times, location data indicating a location of a point when pieces of point cloud data measured before the measurement time were integrated in a time-series order, and reference data assigned to each piece of location data, for specifying the point cloud data including the point indicated by the location data

Abstract: An encoding and decoding device includes: an encoding unit configured to convert input data into an encoded feature vector; a quantization accuracy derivation unit configured to derive quantization accuracy for each encoded feature which is an element of the encoded feature vector in accordance with an encoded code amount, a quantization unit configured to generate a quantized encoded feature vector with a size of a quantized code amount which targets the encoded code amount by executing quantization processing on the encoded feature vector based on the quantization accuracy; and a binarization unit configured to generate encoded data by performing binarization processing on the quantized encoded feature vector.

Abstract: A reference specifying unit specifies reference values corresponding to a plurality of points of point cloud data. A residual decoding unit decodes attribute residuals of the plurality of points from encoded residual data obtained by encoding the attribute residuals, the attribute residuals of the plurality of points being residuals of attribute values of the plurality of points with respect to the reference values. An attribute value calculation unit calculates, for each of the plurality of points, an attribute value of the point from an attribute residual and a reference value.

Abstract: According to an aspect of the present invention, there is provided a learning device including: a classification unit that classifies latent variables obtained from learning data used for learning into a label feature quantity and a non-label feature quantity; a decoding unit that decodes the label feature quantity and the non-label feature quantity classified by the classification unit by using decoder parameters to generate reconstruction data; and an optimization unit that optimizes the decoder parameters to minimize a classification error between the label feature quantity and label information used for classification by using the label feature quantity, and minimize a reconstruction error by using the label feature quantity and the non-label feature quantity.

Abstract: A decoding method is a decoding method performed by a decoding device, the decoding method including steps of: acquiring, from among a plurality of slice numbers indicating a plurality of slices associated with encoded data of the entire point cloud distributed in a three-dimensional space, a first slice number indicating a first slice associated with encoded data of a point cloud of a first space that is a portion of the three-dimensional space, on the basis of a pointer indicating the first slice; and acquiring the encoded data of the point cloud of the first space on the basis of the first slice number, and decoding point cloud data of the first space from the encoded data of the point cloud of the first space.

Abstract: According to an aspect of the present invention, there is provided a learning device including: a classification unit that classifies latent variables, which are feature quantities obtained from learning data used for learning, by using a label feature quantity having label information used for classification; a decoding unit that decodes the latent variables to generate reconstruction data by using predetermined decoding parameters; and an optimization unit that optimizes the decoding parameters to minimize a classification error between the label feature quantity and the label information by using the label feature quantity.

Abstract: A noise determination apparatus includes an acquisition unit and a determination unit. The acquisition unit acquires n-branch tree structure data. The n-branch tree structure data includes information on the presence or absence of a point for a divided region obtained by dividing, by an n-branch tree structure including a plurality of layers, a spatial region represented by point cloud data including information on a position of a point, and includes, for the divided region including only one point, a sign representing the point instead of information on a divided region in a layer lower than the divided region. The determination unit determines a point represented by the sign to be noise in a layer higher than a predetermined layer.

Abstract: A data update method for replacing at least part of encoded point cloud data in which presence or absence of a point is represented by a multi-tree structure for an individual divided region of a region indicated by point cloud data without decoding the multi-tree structure includes acquiring replacement destination data, acquiring replacement source data, which is the encoded point cloud data of the individual divided region corresponding to data to be replaced, and replacing the replacement source data with the replacement destination data.

Abstract: One aspect of the present invention is a data generation method which generates data based on a predetermined estimation model, the data generation method includes a generation step of generating data estimated as a predetermined label by the estimation model and provided with the predetermined label, and generated data has at least either one of a feature close to data to which a label different from the predetermined label is imparted or a feature different from known data to which the predetermined label is imparted.

Abstract: A parameter optimization method includes extracting a feature vector using input data, acquiring a classification result of the feature vector and a class representative vector of every class serving as a classification target, and optimizing a parameter used in the extracting based on a classification error obtained using correct answer data and the classification result and a distance error between the class representative vectors such that areas of features of the classes in a feature space do not overlap each other.

Abstract: Disclosed is a decoding method for decoding encoded data obtained by encoding a cube that includes three-dimensional point cloud data. The decoding method includes a step of obtaining point occupation states of solids obtained after division in one direction, from the encoded data, wherein the direction of the division is determined based on correlation among the point occupation states of the solids after the division. Another decoding method and a decoding apparatus are also disclosed.

Abstract: A decoding method executed by a decoding device for decoding encoded data from point cloud data includes: acquiring an occupancy code represented in an N (1?N?8)-ary tree structure by decoding the encoded data; repeating a process of generating an N-ary tree structure until a block has a predetermined size, the process being a process in which a cube configured to include all the point cloud data is divided into eight blocks and thereafter when a bit of an occupancy code corresponding to a divided block is 1, the block is further divided; and acquiring, as coordinates of a point in the point cloud data, coordinates of a block of the predetermined size where a bit of an occupancy code corresponding to the block of the predetermined size is 1. In the repeating, the decoding device determines a value of N of the N-ary tree structure for a block to be divided on a basis of an inclusion relation between the block to be divided and a non-occupancy region set in advance.

Abstract: A deep feature generation unit (20) inputs an inference image from an input layer (21) of a neural network, performs forward propagation in the neural network, and outputs a plurality of frame images that each include a channel image and are aligned in a predetermined first sequence as intermediate output values from an intermediate layer (22), which is a predetermined layer that is not an output layer of the neural network. A rearrangement unit (30) rearranges the frame images aligned in the first sequence into frame images in a second sequence based on a predetermined rearrangement sequence from the first sequence to the second sequence, such that a total of degrees of similarity between adjacent frame images in the second sequence is greater than a total of degrees of similarity between adjacent frame images in the first sequence.

Abstract: An encoding device includes a division unit that acquires three-dimensional data representing positions of a plurality of points distributed along a surface of an object in a three-dimensional space and divides a parent space including the points in the three-dimensional space into a plurality of child spaces and an encoding unit that changes, based on a position of a target space, which is the child space, to which a sign representing whether the points are included is allocated, according to whether the points are included in a first child space adjacent to the target space, processing for allocating the sign to the target space and a second child space adjacent to the target space.

Abstract: A coding apparatus is a coding apparatus for coding an original image and includes: a division section that divides the original image into blocks that are a plurality of areas to acquire the plurality of blocks; a determination section that, for each of the blocks, determines whether or not to determine the block as an interpolation target; and a substitution section that substitutes a value of a pixel included in the block determined as the interpolation target, with a value that decreases a code amount of the block determined as the interpolation target. The determination section determines whether or not to determine the area that is a target of the determination as an area that is the interpolation target, using an evaluation based on an accuracy of prediction of an image of the block by intra prediction or inter prediction and a degree of the area that is the interpolation target being a generated one.

Abstract: An encoding device includes: an association processing unit that associates first encoded data which is encoded data of an original image with second encoded data which is encoded data of a decoded image which is a result of decoding of the encoded data of the original image; and an encoding unit that encodes, based on the result of associating the first encoded data with the second encoded data, a target image which is an image to be encoded.