Hash-based accessing of geometry occupancy information for point cloud coding

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

receiving data corresponding to a point cloud;

identifying hash elements corresponding to one or more neighboring nodes associated with a current node, wherein the hash elements are stored in a hash table, and wherein the hash table is configured to store hash elements for a plurality of nodes at a same partition depth of an octree associated with the point cloud;

based on determining that parent level context information is not utilized, decreasing a size of the hash table based on deleting one or more of the hash elements corresponding to non-border regions of the one or more neighboring nodes; and

decoding the data corresponding to the point cloud based on the hash table having the decreased size.

2. The method of claim 1 , wherein a maximum size of the hash table is defined for the data corresponding to the point cloud.

3. The method of claim 2 , wherein the maximum is signaled in a sequence parameter set, a geometry parameter set, or a slice header.

4. The method of claim 2 , wherein the hash table is decreased by removing partial elements from the hash table based on the hash table reaching the maximum size.

5. The method of claim 1 , wherein the hash table is decreased based on a Morton order associated with the hash elements.

6. The method of claim 1 , wherein a boundary size associated with the one or more neighboring nodes is signaled in a sequence parameter set, a geometry parameter set, or a slice header.

7. A computer system for decoding point cloud data, the computer system comprising:

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

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

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

identifying code configured to cause the one or more computer processors to identify hash elements corresponding to one or more neighboring nodes associated with a current node, wherein the hash elements are stored in a hash table, and wherein the hash table is configured to store hash elements for a plurality of nodes at a same partition depth of an octree associated with the point cloud;

decreasing code configured to cause the one or more computer processors to, based on determining that parent level context information is not utilized, decrease a size of the hash table based on deleting one or more of the hash elements corresponding to non-border regions of the one or more neighboring nodes; and

decoding code configured to cause the one or more computer processors to decode the data corresponding to the point cloud based on the hash table having the decreased size.

8. The computer system of claim 7 , wherein a maximum size of the hash table is defined for the data corresponding to the point cloud.

9. The computer system of claim 8 , wherein the maximum is signaled in a sequence parameter set, a geometry parameter set, or a slice header.

10. The computer system of claim 8 , wherein the hash table is decreased by removing partial elements from the hash table based on the hash table reaching the maximum size.

11. The computer system of claim 7 , wherein the hash table is decreased based on a Morton order associated with the hash elements.

12. The computer system of claim 7 , wherein a boundary size associated with the one or more neighboring nodes is signaled in a sequence parameter set, a geometry parameter set, or a slice header.

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

receive data corresponding to a point cloud;

identify hash elements corresponding to one or more neighboring nodes associated with a current node, wherein the hash elements are stored in a hash table, and wherein the hash table is configured to store hash elements for a plurality of nodes at a same partition depth of an octree associated with the point cloud;

based on determining that parent level context information is not utilized, decrease a size of the hash table based on deleting one or more of the hash elements corresponding to non-border regions of the one or more neighboring nodes; and

decode the data corresponding to the point cloud based on the hash table having the decreased size.

14. The computer readable medium of claim 13 , wherein a maximum size of the hash table is defined for the data corresponding to the point cloud.

15. The computer readable medium of claim 14 , wherein the maximum is signaled in a sequence parameter set, a geometry parameter set, or a slice header.

16. The computer readable medium of claim 14 , wherein the hash table is decreased by removing partial elements from the hash table based on the hash table reaching the maximum size.

17. The computer readable medium of claim 13 , wherein the hash table is decreased based on a Morton order associated with the hash elements.

18. The computer readable medium of claim 13 , wherein a boundary size associated with the one or more neighboring nodes is signaled in a sequence parameter set, a geometry parameter set, or a slice header.