A three-dimensional data encoding method includes: determining whether to encode each of data units using both or one of an N-ary tree and a prediction tree, each of the data units including three-dimensional points, N being an integer greater than or equal to 2; and generating a bitstream by encoding position information items of the three-dimensional points included in each of the data units, using the both or one of the N-ary tree and the prediction tree determined.

A three-dimensional data encoding method includes: dividing three-dimensional points included in point cloud data into processing units each of which includes one or more of the three-dimensional points; and encoding attribute information of a current three-dimensional point included in a current processing unit, by reference to an encoded processing unit, to generate a bitstream.

Disclosed herein are a point cloud data transmission method including encoding point cloud data, and transmitting a bitstream containing the point cloud data, and a point cloud data reception method including receiving a bitstream containing point cloud data, and decoding the point cloud data.

A system comprises an encoder configured to compress attribute information and/or spatial for a point cloud and/or a decoder configured to decompress compressed attribute and/or spatial information for the point cloud. To compress the attribute and/or spatial information, the encoder is configured to convert a point cloud into an image based representation. Also, the decoder is configured to generate a decompressed point cloud based on an image based representation of a point cloud.

A method, system, and non-transitory computer readable medium for encoding a privacy masked image into an encoded image frame of a video stream is disclosed. The encoding comprises receiving pixel divided image data of an image; receiving data defining an area and graphical characteristic of a privacy mask that masks part of the image; extending the privacy mask to be aligned with one or more encoding units having one or more pixels located within the privacy mask to form a redefined privacy mask; generating a privacy masked image by applying the redefined privacy mask and the graphical characteristic of the privacy mask to the image; and encoding the privacy masked image into the encoded image frame of the video stream, wherein the encoding comprises preventing spatial prediction encoding and/or temporal prediction encoding of pixels within the redefined privacy mask of the image in response to certain conditions.

A method of adaptive point cloud attribute coding includes obtaining an attribute of a current point included in point cloud data, and obtaining candidate predicted values of the obtained attribute, the candidate predicted values including any one or any combination of a weighted average value of a plurality of distances from the current point respectively to other points included in the point cloud data, a first predicted value of a first distance from the current point to a first nearest point among the other points and a second predicted value of a second distance from the current point to a second nearest point after the first nearest point among the other points. The method further includes selecting, for the obtained attribute, one among the obtained candidate predicted values, using rate-distortion optimization, and setting, for a decoder, a flag indicating whether the obtained candidate predicted values includes the weighted average value.

Disclosed herein are a point cloud data transmission method including encoding point cloud data, and transmitting a bitstream containing the point cloud data, and a point cloud data processing method including receiving a bitstream containing point cloud data, and decoding the point cloud data.

Described is a nearest neighbor search method. The method is applicable in a device and comprises: acquiring a Morton code set of point cloud data to be searched; stratifying the point cloud data on the basis of the Morton code set and of a first distance threshold to produce current stratum data; shifting to the right by a first preset digit Morton codes of prediction data other than the stratified data in the point cloud data to produce a corresponding first parent node set; determining, on the basis of Morton codes of the current stratum data, a neighbor area satisfying a criterion in the first parent node set; and determining a nearest neighboring point set of the current stratum data in the neighboring area. Also provided in an exemplary embodiment of the present application are a device and a computer storage medium.

Techniques of compressing triangular mesh data involve generating a neighborhood table (i.e., a table) of fixed size that represents a neighborhood of a predicted vertex of a triangle within a triangular mesh for input into a machine-learning (ML) engine. For example, such a neighborhood table as input into a ML engine can output a prediction for a value (e.g., a position) of a vertex. The residual between the prediction and the actual value of the vertex is stored in an array. The data in the array representing the residuals may be compressed and transmitted over a network. Upon receipt by a computer, the array may be decompressed by the computer. Obtaining the actual value involves the receiving computer generating the same neighborhood table, inputting that neighborhood table into the same ML engine to produce the predicted value, and adding the predicted value to the residual from the decompressed file.

There is provided a computer-implemented method and a decompression unit for decompressing a compressed block of data in accordance with a multi-level difference table. The compressed block of data represents a block of image data comprising a plurality of image element values, wherein each level of the multi-level difference table comprises a plurality of entries. An origin value for the block of image data is determined using data representing the origin value from the compressed block of data. A level within the multi-level difference table for the block of image data is identified using an indication of the level from the compressed block of data. For each image element value in the block of image data, a respective entry indication from the compressed block of data is used to identify one of the entries at the identified level within the multi-level difference table, and the image element value is determined using: (i) the determined origin value for the block of image data, and (ii) the identified entry at the identified level within the multi-level difference table for the image element value.