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US-20260127772-A1 - POINT CLOUD DATA TRANSMISSION DEVICE, POINT CLOUD DATA TRANSMISSION METHOD, POINT CLOUD DATA RECEPTION DEVICE, AND POINT CLOUD DATA RECEPTION METHOD

US20260127772A1US 20260127772 A1US20260127772 A1US 20260127772A1US-20260127772-A1

Abstract

A point cloud data transmission method, according to embodiments, may comprise the steps of: encoding point cloud data; and transmitting a bitstream including the point cloud data. A point cloud data reception method according to the embodiments may comprise the steps of: receiving a bitstream comprising point cloud data; and decoding the point cloud data.

Inventors

  • Hyunmook Oh

Assignees

  • LG ELECTRONICS INC.

Dates

Publication Date
20260507
Application Date
20231018
Priority Date
20221019

Claims (15)

  1. 1 . A method of transmitting point cloud data, comprising: encoding point cloud data; and transmitting a bitstream containing the point cloud data.
  2. 2 . The method of claim 1 , wherein the encoding of the point cloud data comprises: encoding attributes of the point cloud data; and wherein the encoding of the attributes comprises: generating a layer group based on a Level of Detail (LOD) for the attributes; and searching for neighbor candidates of the attributes based on neighbor nodes of a subgroup in the layer group.
  3. 3 . The method of claim 1 , wherein the encoding of the point cloud data comprises: encoding attributes of the point cloud data, wherein the encoding of the attributes comprises: generating a layer group comprising at least one subgroup based on a Level of Detail (LOD) for the attributes; and generating information indicating a relationship between at least one point of the point cloud data and the at least one subgroup.
  4. 4 . The method of claim 3 , wherein the encoding of the attributes further comprises: performing lifting transform on the attributes, wherein context information, zero-run information, and arithmetic encoding information for the encoding for each of the at least one subgroup are updated, wherein the attributes in the layer group are arithmetically encoded by accumulating the zero-run information related to the LOD at a boundary between a first subgroup and a second subgroup included in the layer group.
  5. 5 . The method of claim 2 , wherein the encoding of the attributes further comprises: performing predicting transform on the attributes based on quantization weights, wherein the quantization weights are generated based on whether a current node is referenced as a neighbor node.
  6. 6 . The method of claim 5 , wherein the encoding of the attributes further comprises: correcting the quantization weights; wherein the quantization weights are corrected based on the number of points included in the subgroup in the layer group, wherein the bitstream contains a luma-related quantization parameter and a chroma-related quantization parameter for the layer group.
  7. 7 . A device for transmitting point cloud data transmission, comprising: an encoder configured to encode point cloud data; and a transmitter configured to transmit a bitstream containing the point cloud data.
  8. 8 . A method of receiving point cloud data, the method comprising: receiving a bitstream containing point cloud data; and decoding the point cloud data.
  9. 9 . The method of claim 8 , wherein the decoding of the point cloud data comprises: decoding attributes of the point cloud data, wherein the decoding of the attributes comprises: generating a layer group based on a Level of Detail (LOD) for the attributes; and searching for neighbor candidates of the attributes based on neighbor nodes of a subgroup in the layer group.
  10. 10 . The method of claim 8 , wherein the decoding of the point cloud data comprises: decoding attributes of the point cloud data, wherein the decoding of the attributes comprises: generating a layer group comprising at least one subgroup based on a Level of Detail (LOD) for the attributes; and generating information indicating a relationship between at least one point of the point cloud data and the at least one subgroup.
  11. 11 . The method of claim 10 , wherein the decoding of the attributes further comprises: performing lifting transform on the attributes, wherein context information, zero-run information, and arithmetic decoding information for the decoding for each of the at least one subgroup are updated, wherein the attributes in the layer group are arithmetically decoded by accumulating the zero-run information related to the LOD at a boundary between a first subgroup and a second subgroup included in the layer group.
  12. 12 . The method of claim 9 , wherein the decoding of the attributes further comprises: performing predicting transform on the attributes based on quantization weights, wherein the quantization weights are generated based on whether a current node is referenced as a neighbor node.
  13. 13 . The method of claim 12 , wherein the decoding of the attributes further comprises: correcting the quantization weights; wherein the quantization weights are corrected based on the number of points included in the subgroup in the layer group, wherein the bitstream contains a luma-related quantization parameter and a chroma-related quantization parameter for the layer group.
  14. 14 . The method of claim 8 , wherein the bitstream contains coefficient information related to a weight for a subgroup containing the point cloud data.
  15. 15 . A device for receiving point cloud data, comprising: a receiver configured to receive a bitstream containing point cloud data; and a decoder configured to decode the point cloud data.

Description

TECHNICAL FIELD Embodiments relate to a method and device for processing point cloud content. BACKGROUND ART 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. The point cloud content may express media configured in three dimensions, and is used to provide various services such as virtual reality (VR), augmented reality (AR), mixed reality (MR), and self-driving services. However, tens of thousands to hundreds of thousands of point data are required to represent point cloud content. Therefore, there is a need for a method for efficiently processing a large amount of point data. DISCLOSURE Technical Problem Embodiments provide a device and method for efficiently processing point cloud data. Embodiments provide a point cloud data processing method and device for addressing latency and encoding/decoding complexity. The technical scope of the embodiments is not limited to the aforementioned technical objects, and may be extended to other technical objects that may be inferred by those skilled in the art based on the entire contents disclosed herein. Technical Solution In one aspect of the present disclosure, a method of transmitting point cloud data may include encoding point cloud data, and transmitting a bitstream containing the point cloud data. In another aspect of the present disclosure, a method of receiving point cloud data may include receiving a bitstream containing point cloud data, and decoding the point cloud data. Advantageous Effects Devices and methods according to embodiments may process point cloud data with high efficiency. The devices and methods according to the embodiments may provide a high-quality point cloud service. The devices and methods according to the embodiments may provide point cloud content for providing general-purpose services such as a VR service and a self-driving service. DESCRIPTION OF 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 application, illustrate embodiment(s) of the disclosure and together with the description serve to explain the principle of the disclosure. For a better understanding of various embodiments described below, reference should be made to the description of the following embodiments in connection with the accompanying drawings. The same reference numbers will be used throughout the drawings to refer to the same or like parts. FIG. 1 shows an exemplary point cloud content providing system according to embodiments; FIG. 2 is a block diagram illustrating a point cloud content providing operation according to embodiments; FIG. 3 illustrates an exemplary point cloud encoder according to embodiments; FIG. 4 shows an example of an octree and occupancy code according to embodiments; FIG. 5 illustrates an example of point configuration in each LOD according to embodiments; FIG. 6 illustrates an example of point configuration in each LOD according to embodiments; FIG. 7 illustrates a point cloud decoder according to embodiments; FIG. 8 illustrates a transmission device according to embodiments; FIG. 9 illustrates a reception device according to embodiments; FIG. 10 illustrates an exemplary structure operable in connection with point cloud data transmission/reception methods/devices according to embodiments; FIG. 11 illustrates a process of encoding, transmitting, and decoding point cloud data according to embodiments; FIG. 12 illustrates a layer-based point cloud data configuration according to embodiments and a geometry and attribute bitstream structure according to embodiments; FIG. 13 illustrates bitstream configurations according to embodiments; FIG. 14 illustrates a bitstream sorting method according to embodiments; FIG. 15 illustrates a method of selecting geometry data and attribute data according to embodiments; FIG. 16 illustrates a method of configuring slices containing point cloud data according to embodiments; FIG. 17 illustrates a geometry coding layer structure according to embodiments; FIG. 18 illustrates a layer group and subgroup structure according to embodiments; FIG. 19 illustrates a multi-resolution, multi-size ROI according to embodiments; FIG. 20 illustrates layer group slices according to embodiments; FIG. 21 illustrates a nearest neighbor search process according to embodiments; FIG. 22 illustrates a nearest neighbor search process according to embodiments; FIG. 23 illustrates a nearest neighbor search process according to embodiments; FIG. 24 illustrates an attribute encoding method according to embodiments; FIG. 25 illustrates an attribute decoding method according to embodiments; FIG. 26 illustrates a bitstream containing parameters and encoded point cloud data according to embodiments; FIG. 27 illustrates a sequence parameter set according to embodiments; FIG. 28 illustrates an attribute parameter set and an a