CN-122029822-A - Point cloud data encoding device, point cloud data encoding method, point cloud data decoding device, and point cloud data decoding method

Abstract

The decoding method according to an embodiment may include the steps of receiving a bitstream including point cloud data and decoding the point cloud data. The encoding method according to an embodiment may include the steps of encoding point cloud data and transmitting a bitstream including the point cloud data.

Inventors

• XU HUIZHEN

Assignees

• LG电子株式会社

Dates

Publication Date

20260512

Application Date

20241016

Priority Date

20231016

Claims (15)

1. 1. A decoding method, comprising: Receiving a bit stream containing point cloud data, and And decoding the point cloud data.
2. 2. The decoding method of claim 1, wherein the point cloud data includes roads and objects, Wherein the roadway and the object are captured by LiDAR.
3. 3. The decoding method of claim 1, wherein the decoding of the point cloud data comprises: Decoding geometrical data of the point cloud data, and Decoding attribute data of the point cloud data, Wherein decoding of the geometric data comprises: Generating a prediction unit from the point cloud data; generating a prediction tree based on the prediction unit, and Prediction is performed on the geometric data based on the prediction tree.
4. 4. A decoding method according to claim 3, wherein the type of prediction unit comprises at least one of a road, a static object, a dynamic object, or data with a mix of the road, the static object, and the dynamic object.
5. 5. The decoding method according to claim 3, wherein the coordinate system for the prediction unit is changed to a spherical coordinate system based on the type of the prediction unit being the road, Wherein the prediction tree is generated based on azimuth angles of the point cloud data belonging to the laser index.
6. 6. The decoding method according to claim 3, wherein, based on the type of the prediction unit being the object, a coordinate system for the prediction unit is changed to a spherical coordinate system, Wherein: generating the prediction tree based on azimuth angles of the point cloud data belonging to the laser index, or The prediction tree is generated based on laser indices of the point cloud data belonging to azimuth bin.
7. 7. The decoding method of claim 6, wherein the coordinate system for the prediction unit is a Cartesian coordinate system based on an axis of a bounding box of the prediction unit, the Cartesian coordinate system is rotated, Wherein the prediction tree is generated based on the axes of the point cloud data of bins belonging to a specific axis of the rotated coordinate system.
8. 8. The decoding method of claim 3, wherein a prediction value for current point cloud data is generated based on the prediction tree, Wherein the predicted value is generated based on at least one of a parent node within the predicted tree or a parent node of the parent node, Wherein the predicted value is generated based on the bin of the point cloud data.
9. 9. The decoding method of claim 8, wherein the predicted value is generated from the parent node within the prediction tree based on an interval for the point cloud data based on the type of the prediction unit being the road.
10. 10. The decoding method of claim 1, wherein the bitstream comprises at least one of: Information indicating a first prediction unit generation type; information indicating a second prediction unit generation type; Information indicating a tree-based second prediction unit generation type; Coordinate system type information; frame characteristic information; Predicting tree generation information; predicting method information; Information indicating the axis of bin; information indicating the size of the bin; information indicating a predicted tree traversal order; scaling factor or Interval information.
11. 11. A decoding apparatus comprising: Memory, and At least one processor, the at least one processor being coupled to the memory, Wherein the at least one processor is configured to: Receiving a bit stream containing point cloud data, and And decoding the point cloud data.
12. 12. A method of encoding, comprising: encoding point cloud data, and And sending a bit stream containing the point cloud data.
13. 13. The encoding method of claim 12, wherein the encoding of the point cloud data comprises: encoding geometrical data of the point cloud data, and Encoding attribute data of the point cloud data, Wherein the encoding of the geometric data comprises: Generating a prediction unit from the point cloud data; generating a prediction tree based on the prediction unit, and Prediction is performed on the geometric data based on the prediction tree.
14. 14. The encoding method of claim 13, wherein the type of prediction unit comprises at least one of a road, a static object, a dynamic object, or data having a mix of the road, the static object, and the dynamic object.
15. 15. An encoding apparatus, comprising: Memory, and At least one processor, the at least one processor being coupled to the memory, Wherein the at least one processor is configured to: encoding point cloud data, and And sending a bit stream containing the point cloud data.

Description

Point cloud data encoding device, point cloud data encoding method, point cloud data decoding device, and point cloud data decoding method Technical Field Embodiments relate to a method and apparatus for processing point cloud content. Background The point cloud content is content represented by a point cloud, which is a set of points belonging to a coordinate system representing a three-dimensional space (or capacity). The point cloud content may represent media configured in three dimensions and is used to provide various services such as Virtual Reality (VR), augmented Reality (AR), mixed Reality (MR), XR (augmented reality), and self-driving services. However, tens of thousands to hundreds of thousands of point data are required to represent the point cloud content. Therefore, a method of efficiently processing a large amount of point data is required. Disclosure of Invention Technical problem Embodiments provide an apparatus and method for efficiently processing point cloud data. The embodiment provides a point cloud data processing method and device for solving the latency and encoding/decoding complexity. The technical scope of the embodiments is not limited to the above technical objects, and may be extended to other technical objects that can be inferred by those skilled in the art based on the entire disclosure herein. Technical proposal A decoding method according to an embodiment may include receiving a bitstream including point cloud data and decoding the point cloud data. An encoding method according to an embodiment may include encoding point cloud data and transmitting a bitstream containing the point cloud data. Advantageous effects The device and the method can effectively process the point cloud data. The device and the method can provide high-quality point cloud service. Apparatuses and methods according to embodiments may provide point cloud content for providing general services such as VR services and self-driving services. Drawings The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the principles of the disclosure. For a better understanding of the various embodiments described below, reference should be made to the description of the embodiments below, taken in conjunction with the accompanying drawings. The same reference numbers will be used throughout the drawings to refer to the same or like parts. FIG. 1 illustrates an exemplary point cloud content providing system according to an embodiment; FIG. 2 is a block diagram illustrating a point cloud content providing operation according to an embodiment; FIG. 3 illustrates an exemplary point cloud encoder according to an embodiment; FIG. 4 illustrates an example of an octree and occupancy code according to an embodiment; FIG. 5 illustrates an example of a point configuration in each LOD in accordance with an embodiment; FIG. 6 illustrates an example of a point configuration in each LOD in accordance with an embodiment; FIG. 7 illustrates a point cloud decoder according to an embodiment; fig. 8 shows a transmitting apparatus according to an embodiment; fig. 9 shows a receiving apparatus according to an embodiment; Fig. 10 illustrates an exemplary structure operable in conjunction with a point cloud data transmission/reception method/apparatus according to an embodiment; FIG. 11 illustrates point cloud data including roads and objects, according to an embodiment; FIG. 12 illustrates point cloud data including roads and objects, according to an embodiment; FIG. 13 illustrates types of Prediction Units (PUs) according to an embodiment; FIG. 14 illustrates generation of a prediction tree for a road type according to an embodiment; FIG. 15 illustrates generation of a prediction tree for a road type according to an embodiment; FIG. 16 illustrates an example of object rotation according to an embodiment; FIG. 17 illustrates the generation of a prediction tree for an object type; FIG. 18 illustrates a point cloud data encoder according to an embodiment; FIG. 19 illustrates a point cloud data decoder according to an embodiment; FIG. 20 illustrates a bitstream and parameter set containing point cloud data according to an embodiment; FIG. 21 illustrates a Geometric Parameter Set (GPS) according to an embodiment; FIG. 22 illustrates a Tile Parameter Set (TPS) according to an embodiment; FIG. 23 illustrates a geometric slice header according to an embodiment; FIG. 24 illustrates a geometric PU header according to an embodiment; FIG. 25 illustrates a predictive tree node, according to an embodiment; FIG. 26 illustrates a point cloud data encoding method according to an embodiment, and Fig. 27 illustrates a point cloud data decoding method according to an embodiment. Detailed Description Reference will now be made in detail to the preferred embod