Point cloud data transmission device, point cloud data transmission method, point cloud data reception device, and point cloud data reception method

1 . A method comprising:

decoding geometry data of point cloud data in a bitstream; and

decoding attribute data of the point cloud data in the bitstream, wherein the decoding the attribute data includes:

predicting the attribute data based on a search range for a neighbor,

wherein the neighbor is selected based on a distance,

wherein the bitstream includes information related to the search range of points around a search center point, and information for a threshold which is used to predict the attributed data.

2 . The method of claim 1 ,

wherein the decoding of the attribute data comprises:

generating a Level of Detail (LOD) based on the geometry data and the attribute data;

generating a neighbor point set for a point of the point cloud data based on a similar attribute;

selecting the neighbor in the neighbor point set; and

decoding information about the selected neighbor.

3 . The method of claim 2 , wherein the generating of the neighbor point set comprises:

configuring the search range for searching the neighbor point set based on a Morton code or an octree for the point cloud data.

4 . The method of claim 3 , wherein the selecting of the neighbor comprises:

selecting the neighbor based on a distance, a similar attribute, or both the distance and the similar attribute within the search range.

5 . The method of claim 2 , further comprising:

generating a predicted candidate list for the attribute data;

parsing an index of the predicted candidate list of the point cloud data; and

restoring an index difference value based on metadata included in the bitstream.

6 . An apparatus comprising:

a memory; and

at least one processor connected to the memory, at least one processor configured to:

receive a bitstream containing point cloud data;

decode geometry data of the point cloud data; and

decode attribute data of the point cloud data

predict the attribute data based on a search range for a neighbor,

wherein the neighbor is selected based on a distance,

wherein the bitstream includes information related to the search range of points around a search center point, and information for a threshold which is used to predict the attributed data.

7 . The apparatus of claim 6 ,

wherein the at least one processor is further configured to:

generate a Level of Detail (LOD) based on the geometry data and the attribute data;

generate a neighbor point set for points of the point cloud data based on similar attributes;

select the neighbor in the neighbor point set; and

decode information about the selected neighbor.

8 . The apparatus of claim 7 , wherein the at least one processor is further configured to:

configure the search range for searching the neighbor point set based on a Morton code or an octree for the point cloud data.

9 . The apparatus of claim 8 , wherein the at least one processor is further configured to:

select the neighbor based on a distance, a similar attribute, or both the distance and the similar attribute within the search range.

10 . The apparatus of claim 7 , wherein the at least one processor is further configured to:

generate a predicted candidate list for the attribute data;

parse an index of the predicted candidate list of the point cloud data; and

restore an index difference value based on metadata included in the bitstream.

11 . A method comprising:

encoding geometry data of point cloud data in a bitstream;

encoding attribute data of the point cloud data in the bitstream, wherein the encoding the attribute data includes:

predicting the attribute data based on a search range for a neighbor,

wherein the neighbor is selected based on a distance;

wherein the bitstream includes information related to the search range of points around a search center point, and information for a threshold which is used to predict the attributed data.

12 . The method of claim 11 ,

wherein the encoding of the attribute data comprises:

generating a Level of Detail (LOD) based on the geometry data and the attribute data;

generating a neighbor point set for points of the point cloud data based on a similar attribute;

selecting the neighbor in the neighbor point set; and

encoding information about the selected neighbor.

13 . The method of claim 12 , wherein the generating of the neighbor point set comprises:

configuring the search range for searching the neighbor point set based on a Morton code or an octree for the point cloud data.

14 . The method of claim 13 , wherein the selecting of the neighbor comprises:

selecting the neighbor based on a distance, a similar attribute, or both the distance and the similar attribute within the search range.

15 . The method of claim 12 , further comprising:

generating a predicted candidate list for the attribute data;

selecting a neighbor candidate in the predicted candidate list based on rate-distortion optimization (RDO);

encoding an index difference for the neighbor candidate; and

transmitting metadata about the index difference.

16 . An apparatus comprising:

a memory; and

at least one processor connected to the memory, at least one processor configured to:

encode geometry data of point cloud data;

encode attribute data of the point cloud data

predict the attribute data based on a search range for a neighbor,

wherein the neighbor is selected based on a distance; and

transmit a bitstream containing the point cloud data,

wherein the bitstream includes information related to the search range of points around a search center point, and information for a threshold which is used to predict the attributed data.

17 . The apparatus of claim 16 , wherein the at least one processor is further configured to:

generate a Level of Detail (LOD) based on the geometry data and the attribute data;

generate a neighbor point set for points of the point cloud data based on a similar attribute;

select the neighbor in the neighbor point set; and

encode information about the selected neighbor.

18 . The apparatus of claim 17 , wherein the at least one processor is further configured to:

configure the search range for searching the neighbor point set based on a Morton code or an octree for the point cloud data.

19 . The apparatus of claim 18 , wherein the at least one processor is further configured to:

select the neighbor based on a distance, a similar attribute, or both the distance and the similar attribute within the search range.

20 . The apparatus of claim 17 , wherein the at least one processor is further configured to:

generate a predicted candidate list for the attribute data;

select a neighbor candidate in the predicted candidate list based on rate-distortion optimization (RDO);

encode an index difference for the neighbor candidate; and

transmit metadata about the index difference.