Predictive encoding/decoding method and apparatus for azimuth information of point cloud
An encoding method includes: obtaining original point cloud data; obtaining depth information of a point cloud based on the original point cloud data; establishing a relationship between the depth information and azimuth information of the point cloud; and predictively encoding the azimuth information of the point cloud by using the relationship between the depth information and the azimuth information of the point cloud, to obtain coded stream information.
1 . A predictive encoding method for azimuth information of a point cloud, the method comprising:
obtaining original point cloud data;
obtaining depth information of a point cloud based on the original point cloud data;
establishing a relationship between the depth information and azimuth information of the point cloud, wherein establishing the relationship between the depth information and the azimuth information of the point cloud comprises:
establishing the relationship between the depth information and the azimuth information of the point cloud by using a mathematical derivation method; and
predictively encoding the azimuth information of the point cloud using the relationship between the depth information and the azimuth information of the point cloud, to obtain coded stream information; and
wherein a relational expression between the depth information and the azimuth information of the point cloud is established using the mathematical derivation method as follows:
φ
-
φ
0
=
-
α
-
arc
tan
H
o
r
;
wherein
φ represents azimuth information of a respective point, φ 0 represents originally collected azimuth information of the respective point, r represents depth information of the respective point, α represents a horizontal correction angle of a laser to which the respective point belongs, and H o represents a horizontal offset of the laser to which the respective point belongs.
2 . The predictive encoding method according to claim 1 , wherein the original point cloud data includes a group of 3D spatial points, each spatial point in the group of 3D spatial points records its geometric position information, and the geometric position information of each spatial point is expressed based on a Cartesian coordinate system.
3 . The predictive encoding method according to claim 1 , wherein obtaining the depth information of a point cloud based on the original point cloud data comprises:
calculating the depth information of the point cloud using the following formula:
r 32 √{square root over ( x 2 +y 2 )}; or r =√{square root over ( x 2 +y 2 +z 2 )};
wherein r represents depth information of a respective point in the original point cloud data, and x and y are Cartesian coordinate components of the respective point; or (x, y, z) are Cartesian coordinates of the respective point.
4 . The predictive encoding method according to claim 1 , wherein after the relational expression between the depth information and the azimuth information of the point cloud is obtained, the method further comprises:
selecting several points from points collected by a same laser or encoded points and estimating unknown parameters α and H o in the relational expression based on information about the selected points.
5 . The predictive encoding method according to claim 4 , wherein a formula for estimating the unknown parameters α and H o by selecting two points is:
H
o
=
(
r
1
-
r
2
)
·
(
1
+
tan
Δφ
1
·
tan
Δφ
2
)
±
(
r
1
-
r
2
)
2
·
(
1
+
tan
Δφ
1
tan
Δφ
2
)
2
-
4
(
tan
Δφ
1
-
tan
Δφ
2
)
2
·
r
1
·
r
2
2
(
tan
Δφ
1
-
tan
Δφ
2
)
tan
α
=
r
1
·
tan
Δφ
1
-
r
2
·
tan
Δφ
2
H
o
·
(
tan
Δφ
1
-
tan
Δφ
2
)
-
(
r
1
-
r
2
)
;
wherein
r 1 and r 2 separately represent depth information of the two selected points, and Δφ 1 and Δφ 2 separately represent azimuth residuals of the two selected points.
6 . The predictive encoding method according to claim 1 , wherein predictively encoding the azimuth information of the point cloud using the relationship between the depth information and the azimuth information of the point cloud comprises:
predicting the azimuth of the point cloud based on the relationship between the depth information and the azimuth information of the point cloud, to obtain an initial predicted value of an azimuth residual;
selectively shifting the initial predicted value of the azimuth residual to obtain a final predicted value of the azimuth residual and a prediction residual of the azimuth residual; and
encoding the prediction residual of the azimuth residual and azimuth auxiliary information.
7 . The predictive encoding method according to claim 6 , wherein predicting the azimuth of the point cloud based on the relationship between the depth information and the azimuth information of the point cloud, to obtain the initial predicted value of the azimuth residual, comprises:
obtaining the initial predicted value Δ{circumflex over (φ)} of the azimuth residual Δφ based on a formula as follows:
Δ
φ
ˆ
=
-
α
i
-
arc
tan
H
o
i
r
;
and
wherein H o i represents a horizontal offset of an i th laser to which a current point belongs, α 1 represents a horizontal correction angle of the i th laser to which a current point belongs, and both H o i and α i are calibration parameters of the i th laser.
8 . The predictive encoding method according to claim 7 , wherein selectively shifting the initial predicted value of the azimuth residual to obtain the final predicted value of the azimuth residual and the prediction residual of the azimuth residual comprises:
obtaining a reminder after the initial predicted value Δ{circumflex over (φ)} of the azimuth residual is divided by the resolution φ speed of a sampling angle of a laser radar;
using the reminder to initialize the final predicted value Δ{tilde over (ϕ)} of the azimuth residual Δ{tilde over (ϕ)}=Δ{tilde over (ϕ)}%ϕ speed ;
determining whether Δ{tilde over (ϕ)} is greater than
φ
s
p
e
e
d
2
,
Δ
ϕ
˜
=
Δ
ϕ
˜
-
ϕ
speed
,
and whether Δ{tilde over (ϕ)} is less than
-
φ
s
p
e
e
d
2
,
Δ
ϕ
˜
=
Δ
ϕ
˜
+
ϕ
speed
;
and
calculating the prediction residual res Δφ of the azimuth residual using a formula res Δϕ =Δϕ−Δ{tilde over (ϕ)}.
9 . The predictive encoding method according to claim 6 , wherein encoding the prediction residual of the azimuth residual and azimuth auxiliary information comprises:
using an entropy encoding technology to encode the prediction residual of the azimuth residual; and
encoding the azimuth auxiliary information in a differential encoding manner, wherein the azimuth auxiliary information is an azimuth index j of the point.
10 . A predictive encoding device for azimuth information of a point cloud, the device comprising:
a processor; and
memory storing at least one instruction that, when executed by the at least one processor, cause the device to perform operations comprising:
obtaining original point cloud data;
obtaining depth information of a point cloud based on the original point cloud data;
establishing a relationship between the depth information and azimuth information of the point cloud, wherein establishing the relationship between the depth information and the azimuth information of the point cloud comprises:
establishing the relationship between the depth information and the azimuth information of the point cloud by using a mathematical derivation method; and
predictively encoding the azimuth information of the point cloud using the relationship between the depth information and the azimuth information of the point cloud, to obtain coded stream information; and
wherein a relational expression between the depth information and the azimuth information of the point cloud is established using the mathematical derivation method as follows:
φ
-
φ
0
=
-
α
-
arc
tan
H
o
r
;
wherein
φ represents azimuth information of a respective point, φ 0 represents originally collected azimuth information of the respective point, I represents depth information of the respective point, α represents a horizontal correction angle of a laser to which the respective point belongs, and H o represents a horizontal offset of a laser to which the respective point belongs.
11 . The predictive encoding device according to claim 10 , wherein after the relational expression between the depth information and the azimuth information of the point cloud is obtained, the method further comprises:
selecting several points from points collected by a same laser or encoded points and estimating unknown parameters α and H o in the relational expression based on information about the selected points.
12 . The predictive encoding device according to claim 11 , wherein a formula for estimating the unknown parameters α and H o by selecting two points is:
H
o
=
(
r
1
-
r
2
)
·
(
1
+
tan
Δφ
1
·
tan
Δφ
2
)
±
(
r
1
-
r
2
)
2
·
(
1
+
tan
Δφ
1
tan
Δφ
2
)
2
-
4
(
tan
Δφ
1
-
tan
Δφ
2
)
2
·
r
1
·
r
2
2
(
tan
Δφ
1
-
tan
Δφ
2
)
tan
α
=
r
1
·
tan
Δφ
1
-
r
2
·
tan
Δφ
2
H
o
·
(
tan
Δφ
1
-
tan
Δφ
2
)
-
(
r
1
-
r
2
)
;
wherein
r 1 and r 2 separately represent depth information of the two selected points, and Δφ 1 and Δφ 2 separately represent azimuth residuals of the two selected points.
13 . The predictive encoding device according to claim 10 , wherein predictively encoding the azimuth information of the point cloud using the relationship between the depth information and the azimuth information of the point cloud comprises:
predicting the azimuth of the point cloud based on the relationship between the depth information and the azimuth information of the point cloud, to obtain an initial predicted value of an azimuth residual;
selectively shifting the initial predicted value of the azimuth residual to obtain a final predicted value of the azimuth residual and a prediction residual of the azimuth residual; and
encoding the prediction residual of the azimuth residual and azimuth auxiliary information.
14 . The device according to claim 10 , wherein obtaining the depth information of a point cloud based on the original point cloud data comprises:
calculating the depth information of the point cloud using the following formula:
r 32 √{square root over ( x 2 +y 2 )}; or r =√{square root over ( x 2 +y 2 +z 2 )};
wherein r represents depth information of a respective point in the original point cloud data, and x and y are Cartesian coordinate components of the respective point; or (x, y, z) are Cartesian coordinates of the respective point.
15 . The device according to claim 10 , wherein the original point cloud data includes a group of 3D spatial points, each spatial point in the group of 3D spatial points records its geometric position information, and the geometric position information of each spatial point is expressed based on a Cartesian coordinate system.
16 . A predictive decoding method for azimuth information of a point cloud, comprising:
obtaining coded stream information, and decoding the coded stream information to obtain a prediction residual of an azimuth residual of a point cloud and azimuth auxiliary information;
predicting an azimuth of the point cloud using reconstructed depth information and a relationship between the depth information and azimuth information, to obtain a final predicted value of the azimuth residual;
reconstructing the azimuth residual of the point cloud based on the final predicted value of the azimuth residual and the prediction residual of the azimuth residual; and
reconstructing the azimuth information of the point cloud based on the reconstructed azimuth residual and the azimuth auxiliary information.
17 . The method according to claim 16 , wherein obtaining the coded stream information, and decoding the coded stream information to obtain the prediction residual of the azimuth residual of the point cloud and the azimuth auxiliary information comprises:
obtaining compressed coded stream information and decoding the compressed coded stream information by using entropy decoding, to obtain the prediction residual of the azimuth residual of the point cloud and the azimuth auxiliary information.
18 . The method according to claim 17 , wherein predicting the azimuth of the point cloud using the reconstructed depth information and the relationship between the depth information and the azimuth information, to obtain the final predicted value of the azimuth residual comprises:
predicting azimuth residual information of the point cloud by using depth information of the point cloud and the relationship between the depth information and the azimuth information, to obtain an initial predicted value of the azimuth residual; and
selectively shifting the initial predicted value of the azimuth residual to obtain a final predicted value of the azimuth residual.
19 . The method according to claim 18 , wherein reconstructing the azimuth residual of the point cloud based on the final predicted value of the azimuth residual and the prediction residual of the azimuth residual comprises:
adding the final predicted value of the azimuth residual and the prediction residual of the azimuth residual, to reconstruct the azimuth residual of the point cloud.
20 . The method according to claim 19 , wherein reconstructing the azimuth information of the point cloud based on the reconstructed azimuth residual and the azimuth auxiliary information comprises:
calculating an approximate value of an originally collected azimuth of the point cloud by using a decoded azimuth index; and
adding the reconstructed azimuth residual and the approximate value of the originally collected azimuth, to reconstruct the azimuth information of the point cloud.