Advanced k-D tree encoding for point clouds by most significant axis selection
An encoder includes a processor, a buffer, and a memory. The memory includes code as instructions that cause the processor to perform a number of steps. The steps include partitioning a geometric construct within an axis of the geometric construct based on a point differential between two partitions, the geometric construct including geometric data, determining a number of points in the partition, storing a value indicating the number of points in the buffer, and entropy encoding the value stored in the buffer.
1. An encoder comprising:
a processor;
a buffer; and
a memory, the memory including code as instructions that cause the processor to:
partition a geometric construct within an axis of the geometric construct into a first partition and a second partition based on a point differential between the first partition and the second partition, the geometric construct including geometric data,
determine a first number of points in the first partition,
determine a second number of points in the second partition,
store a value associated with one of the first number of points or the second number of points in the buffer, and
entropy encode the value stored in the buffer.
2. The encoder of claim 1 , further comprising instructions that cause the processor to convert a float or double precision value associated with the geometric data into a b-bit integer representation.
3. The encoder of claim 1 , wherein the geometric data represents a point cloud and the geometric construct is a two or more dimensional shape encompassing the point cloud.
4. The encoder of claim 1 , wherein the partitioning of the geometric construct includes recursively partitioning the geometric construct using a k-D tree.
5. The encoder of claim 1 , wherein the buffer is a first buffer, the encoder further comprising a second buffer, wherein
an algorithm used to partition the geometric construct selects a next axis to partition in a determinable order, and
a value indicating the axis is stored in the second buffer.
6. The encoder of claim 1 , wherein
the value stored in the buffer is a deviation value, and
the deviation value is determined based on a number of points in a parent partition.
7. The encoder of claim 1 , wherein
the partitioning of the geometric construct includes partitioning the geometric construct using a k-D tree and,
the first partition includes a third partition and a fourth partition,
the instructions further cause the processor to:
determine a third number of points in the third partition, and
determine a deviation value based on the first number of points and the third number of points, wherein the value stored in the buffer is the deviation value.
8. A method comprising:
partitioning a geometric construct within an axis of the geometric construct into a first partition and a second partition based on a point differential between the first partition and the second partition, the geometric construct including geometric data;
determining a first number of points in the first partition;
determining a second number of points in the second partition;
storing a value associated with one of the first number of points or the second number of points in a buffer; and
entropy encoding the value stored in the buffer.
9. The method of claim 8 , further comprising converting a float or double precision value associated with the geometric data into a b-bit integer representation.
10. The method of claim 8 , wherein the geometric data represents a point cloud and the geometric construct is a two or more dimensional shape encompassing the point cloud.
11. The method of claim 8 , wherein the partitioning of the geometric construct includes recursively partitioning the geometric construct using a k-D tree.
12. The method of claim 8 , wherein
an algorithm used to partition the geometric construct selects a next axis to partition in a determinable order, and
a value indicating the axis is stored.
13. The method of claim 8 , wherein
the stored value is a deviation value,
the deviation value corresponding to the number of points is determined based on a number of points in a larger partition including the partition.
14. The method of claim 8 , wherein
the partitioning of the geometric construct includes partitioning the geometric construct using a k-D tree, and
the first partition includes a third partition and a fourth partition, the method further comprising:
determining a third number of points in the third partition; and
determining a deviation value based on the first number of points and the third number of points, wherein the stored value is the deviation value.
15. A decoder comprising:
a processor;
a first buffer;
a second buffer;
a third buffer; and
a memory, the memory including code as instructions that cause the processor to:
load the first buffer using data from an encoded geometric data file,
load the second buffer using data from the encoded geometric data file,
load the third buffer using data from the encoded geometric data file,
entropy decode the first buffer,
determine a first partition in a geometric construct to insert data from the first buffer,
insert a first number of points based on the data from the first buffer into the partition,
determine whether the first number of points is greater than a threshold value,
upon determining the first number of points is greater than the threshold value,
read a value from the third buffer,
select an axis to divide the first partition based on the value read from the third buffer, and
divide the first partition within the selected axis to divide to generate a second partition and third partition, and
move a subset of the first number of points to one of the second partition or the third partition based on the data from the first buffer,
upon determining the first number of points is less than or equal to the threshold value,
position a point in the first partition based on data in the second buffer, and
repeat the determining whether the first number of points is greater than the threshold value on the second partition or the third partition replacing the first number of points with the subset of the first number of points and until each partition in the geometric construct includes a number of points less than or equal to the threshold value.
16. The decoder of claim 15 , wherein the entropy decoding of the first buffer includes converting a deviation value corresponding to a number of points in the second partition.
17. The decoder of claim 15 , wherein the geometric data file includes geometric data representing a point cloud and the geometric construct is a two or more dimensional shape encompassing the point cloud.
18. The decoder of claim 15 , wherein determining the first partition and determining the second partition includes building a k-D tree based on the first buffer and the second buffer.
19. The decoder of claim 15 , wherein
the entropy decoding of the first buffer includes converting a deviation value corresponding to a number of points in the second partition, and
the deviation value corresponding to the number of points is determined based on a number of points in a larger partition including the partition.
20. The decoder of claim 15 , wherein the second buffer includes data representing a position of a point in the geometric construct.
21. A method comprising:
storing first data from an encoded geometric data file in a first memory;
storing second data from the encoded geometric data file in a second memory;
storing third data from the encoded geometric data file in a third memory;
entropy decoding the first data;
determining a first partition in a geometric construct to insert data from the first memory;
inserting a first number of points based on the first data into the first partition;
determining whether the first number of points is greater than a threshold value;
upon determining the first number of points is greater than the threshold value,
read a value from the third memory,
select an axis to divide the first partition based on the value read from the third memory,
divide the first partition within the selected axis to divide to generate a second partition and third partition, and
move a subset of the first number of points to one of the second partition or the third partition based on the first data;
upon determining the first number of points is less than or equal to the threshold value,
position a point in the first partition based on the second data; and
repeating the determining whether the first number of points is greater than the threshold value on the second partition or the third partition replacing the first number of points with the subset of the first number of points and until each partition in the geometric construct includes a number of points less than or equal to the threshold value.
22. The method of claim 21 , wherein the entropy decoding of the first data includes converting a deviation value corresponding to a number of points in the second partition.
23. The method of claim 21 , wherein the geometric data file includes geometric data representing a point cloud and the geometric construct is a two or more dimensional shape encompassing the point cloud.
24. The method of claim 21 , wherein determining the first partition and determining the second partition includes building a k-D tree based on the first data and the second data.
25. The method of claim 21 , wherein
the entropy decoding of the first data includes converting a deviation value corresponding to a number of points in the second partition, and
the deviation value corresponding to the number of points is determined based on a number of points in a larger partition including the partition.
26. The method of claim 21 , wherein the second data includes data representing a position of a point in the geometric construct.