The present disclosure relates to an information processing apparatus and method capable of suppressing an increase in load of reproduction processing.

To generate, by using tile identification information indicating a tile of a point cloud corresponding to a data unit of a bitstream of the point cloud expressing an object having a three-dimensional shape as a set of points, tile management information that is information for managing the tile corresponding to a subsample including a single or a plurality of consecutive data units of the bitstream stored as a sample in a file, and to generate the file that stores the bitstream and the tile management information. The present disclosure can be applied to, for example, an information processing apparatus, an information processing method, or the like.

A three-dimensional data encoding method includes: obtaining three-dimensional points; generating a data unit including one or more prediction trees and one or more identification information items, using the three-dimensional points; and generating a bitstream including the data unit. The data unit includes one identification information item out of the one or more identification information items subsequent to one prediction tree out of the one or more prediction trees, and the one identification information item indicates whether there is a subsequent prediction tree subsequent to the one identification information item in the data unit.

A coding method and an apparatus are provided. XR data of different dimensions or different QoS requirements are distinguished during coding.

A coding method and an apparatus are provided. XR data of different dimensions or different QoS requirements are distinguished during coding.

This application provides an image processing method and apparatus, a device, and a computer-readable storage medium for use in the field of computer vision. The image processing method includes the following steps: a first image device obtains a compressed image of a raw image and decompresses the compressed image to obtain a first decompressed image; the first image device determines target difference information based on difference information between the raw image and the first decompressed image; the first image device further compresses the target difference information to obtain compressed target difference information; and the first image device sends the compressed image and the compressed target difference information to a second image device. The second image device restores an image based on the received compressed image and target difference information. This application reduces a precision loss between the image restored by the second image device and the raw image.

This application provides an image processing method and apparatus, a device, and a computer-readable storage medium for use in the field of computer vision. The image processing method includes the following steps: a first image device obtains a compressed image of a raw image and decompresses the compressed image to obtain a first decompressed image; the first image device determines target difference information based on difference information between the raw image and the first decompressed image; the first image device further compresses the target difference information to obtain compressed target difference information; and the first image device sends the compressed image and the compressed target difference information to a second image device. The second image device restores an image based on the received compressed image and target difference information. This application reduces a precision loss between the image restored by the second image device and the raw image.

A point cloud decoding device 200 according to the present invention including: an attribute-information decoding unit 2060 that determines whether or not a chroma signal is included in a bit stream, and avoids decoding for syntax regarding the chroma signal in a case where the chroma signal is determined not to be included in the bit stream.

A three-dimensional data encoding method includes: calculating a prediction residual that is a difference between geometry information of a three-dimensional point included in point cloud data and a predicted value; quantizing the prediction residual using a first quantization parameter; and generating a bitstream including (i) first information indicating the prediction residual quantized, (ii) second information indicating a second quantization parameter, (iii) third information indicating a first difference between the second quantization parameter and the first quantization parameter, (iv) fourth information indicating a first bit count of the first information, and (v) fifth information indicating a second bit count of the fourth information. When the first difference is not zero, the bitstream further includes sixth information indicating an amount of change in the fifth information that is in accordance with the first difference, and when the first difference is zero, the bitstream does not include the sixth information.

A three-dimensional data encoding method includes: calculating a prediction residual that is a difference between geometry information of a three-dimensional point included in point cloud data and a predicted value; quantizing the prediction residual using a first quantization parameter; and generating a bitstream including (i) first information indicating the prediction residual quantized, (ii) second information indicating a second quantization parameter, (iii) third information indicating a first difference between the second quantization parameter and the first quantization parameter, (iv) fourth information indicating a first bit count of the first information, and (v) fifth information indicating a second bit count of the fourth information. When the first difference is not zero, the bitstream further includes sixth information indicating an amount of change in the fifth information that is in accordance with the first difference, and when the first difference is zero, the bitstream does not include the sixth information.

An example computing platform is configured to (i) receive a two-dimensional (2D) image file comprising a construction drawing, (ii) generate, via semantic segmentation, a first set of polygons corresponding to respective areas of the 2D image file, (iii) generate, via instance segmentation, a second set of polygons corresponding to respective areas of the 2D image file, (iv) generate, via unsupervised image processing, a third set of polygons corresponding to respective areas of the 2D image file, (v) based on (a) overlap between polygons in the first, second, and third sets of polygons and (b) respective confidence scores for each of the overlapping polygons, determine a set of merged polygons corresponding to respective areas of the 2D image file, and (vi) cause a client station to display a visual representation of the 2D image file where each merged polygon is overlaid as a respective selectable region of the 2D image file.