Coding of multiple-component attributes for point cloud coding

1. A method of point cloud coding, executable by a processor, comprising:

receiving, from a bitstream, data corresponding to a point cloud;

obtaining from the data a first prediction residual of a first component from among a plurality of components of an attribute associated with the point cloud;

reconstructing the first prediction residual;

determining a predicted second prediction residual based on the reconstructed first prediction residual and at least one model parameter;

obtaining a second prediction residual of a second component from among the plurality of components based on the predicted second prediction residual;

reconstructing the second prediction residual; and

decoding the data corresponding to the point cloud based on the reconstructed first prediction residual and the reconstructed second prediction residual.

2. The method of claim 1 , wherein a model corresponding to the at least one model parameter comprises at least one of a linear model, a quadratic model, and a polynomial model.

3. The method of claim 1 , further comprising:

determining a predicted third prediction residual based on the reconstructed first prediction residual, the reconstructed second prediction residual, and the at least one model parameter;

obtaining a third prediction residual of a third component from among the plurality of components based on the predicted third prediction residual;

reconstructing the third prediction residual; and

decoding the data corresponding to the point cloud based on the reconstructed first prediction residual, the reconstructed second prediction residual, and the reconstructed third prediction residual.

4. The method of claim 3 , wherein the predicted second prediction residual is determined using a following first equation:

p 1 =α 0 R 0 ,

wherein the predicted third prediction residual is determined using a following second equation:

p 2 =α 1 R 0 +β 1 R 1 , and

wherein R 0 represents the reconstructed first prediction residual, p 1 represents the predicted second prediction residual, R 1 represents the reconstructed second prediction residual, p 2 represents the predicted third prediction residual, and

wherein α 0 , α 1 , and β 1 represent model parameters of the at least one model parameter.

5. The method of claim 3 , wherein the predicted second prediction residual is determined using a following first equation:

p 1 =α 0 R 0 ,

wherein the predicted third prediction residual is determined using a following second equation:

p 2 =α 1 R 0 , and

wherein R 0 represents the reconstructed first prediction residual, p 1 represents the predicted second prediction residual, p 2 represents the predicted third prediction residual, and

wherein α 0 and α 1 represent model parameters of the at least one model parameter.

6. The method of claim 3 , wherein the predicted second prediction residual is determined using a following first equation:

p 1 =α 0 R 0 ,

wherein the predicted third prediction residual is determined using a following second equation:

p 2 =β 1 R 1 , and

wherein R 0 represents the reconstructed first prediction residual, p 1 represents the predicted second prediction residual, R 1 represents the reconstructed second prediction residual, p 2 represents the predicted third prediction residual, and

wherein α 0 and β 1 represent model parameters of the at least one model parameter.

7. The method of claim 3 , wherein the predicted second prediction residual is determined using a following first equation:

p 1 =α 0 R 0 ,

wherein the predicted third prediction residual is determined using a following second equation:

p 2 =α 0 R 0 ,

wherein R 0 represents the reconstructed first prediction residual, p 1 represents the predicted second prediction residual, and p 2 represents the predicted third prediction residual, and

wherein α 0 represents a model parameter of the at least one model parameter.

8. The method of claim 3 , wherein the predicted second prediction residual is determined using a following first equation:

p 1 =α 0 R 0 ,

wherein the predicted third prediction residual is determined using a following second equation:

p 2 =α 0 R 1 , and

wherein R 0 represents the reconstructed first prediction residual, p 1 represents the predicted second prediction residual, R 1 represents the reconstructed second prediction residual, p 2 represents the predicted third prediction residual, and

wherein α 0 represents a model parameter of the at least one model parameter.

9. The method of claim 1 , wherein the at least one model parameter is signaled in the data.

10. The method of claim 1 , wherein the at least one model parameter is a predetermined model parameter.

11. The method of claim 1 , wherein the at least one model parameter is adaptively learned based on at least one of the reconstructed first prediction residual and the reconstructed second prediction residual.

12. The method of claim 1 , wherein the first component is associated with a first context model, and

wherein the second component is associated with a second context model different from the first context model.

13. A computer system for point cloud coding, the computer system comprising:

one or more computer-readable non-transitory storage media configured to store computer program code; and

one or more processors configured to access the computer program code and operate as instructed by the computer program code, said computer program code including:

receiving code configured to cause the one or more processors to receive, from a bitstream, data corresponding to a point cloud;

first obtaining code configured to cause the one or more processors to obtain, from the data a first prediction residual of a first component from among a plurality of components of an attribute associated with the point cloud;

first reconstructing code configured to cause the one or more processors to reconstruct the first prediction residual;

first determining code configured to cause the one or more processors to determine a predicted second prediction residual based on the reconstructed first prediction residual and at least one model parameter;

second obtaining code configured to cause the one or more processors to obtain a second prediction residual of a second component from among the plurality of components based on the predicted second prediction residual;

second reconstructing code configured to cause the one or more processors to reconstruct the second prediction residual; and

first decoding code configured to cause the one or more processors to decode the data corresponding to the point cloud based on the reconstructed first prediction residual and the reconstructed second prediction residual.

14. The computer system of claim 13 , wherein a model corresponding to the at least one model parameter comprises at least one of a linear model, a quadratic model, and a polynomial model.

15. The computer system of claim 13 , wherein the computer program code further includes:

third determining code configured to cause the one or more processors to determine a predicted third prediction residual based on the reconstructed first prediction residual, the reconstructed second prediction residual, and the at least one model parameter;

third obtaining code configured to cause the one or more processors to obtain a third prediction residual of a third component from among the plurality of components based on the predicted third prediction residual;

third reconstructing code configured to cause the one or more processors to reconstruct the third prediction residual; and

second decoding code configured to cause the one or more processors to decode the data corresponding to the point cloud based on the reconstructed first prediction residual, the reconstructed second prediction residual, and the reconstructed third prediction residual.

16. The computer system of claim 13 , wherein the at least one model parameter is signaled in the data.

17. The computer system of claim 13 , wherein the at least one model parameter is a predetermined model parameter.

18. The computer system of claim 13 , wherein the at least one model parameter is adaptively learned based on at least one of the reconstructed first prediction residual and the reconstructed second prediction residual.

19. The computer system of claim 13 , wherein the first component is associated with a first context model, and

wherein the second component is associated with a second context model different from the first context model.

20. A non-transitory computer readable medium having stored thereon a computer program for point cloud coding, the computer program configured to cause one or more computer processors to:

receive, from a bitstream, data corresponding to a point cloud;

obtain, from the data a first prediction residual of a first component from among a plurality of components of an attribute associated with the point cloud;

reconstruct the first prediction residual;

determine a predicted second prediction residual based on the reconstructed first prediction residual and at least one model parameter;

obtain a second prediction residual of a second component from among the plurality of components based on the predicted second prediction residual;

reconstruct the second prediction residual; and

decode the data corresponding to the point cloud based on the reconstructed first prediction residual and the reconstructed second prediction residual.