CN112509107A - Point cloud attribute recoloring method, device and encoder - Google Patents
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Abstract
The invention discloses a point cloud attribute recoloring method, a point cloud attribute recoloring device and an encoder, wherein the point cloud attribute recoloring method comprises the following steps: acquiring and processing original point cloud to obtain reconstructed point cloud; finding out association points corresponding to each point in the reconstructed point cloud from the original point cloud to obtain an association point set; combining repeated points of the original point cloud in the associated point set based on the original attribute information of all the points in the associated point set to obtain the attribute information of the processed associated point set; and calculating the attribute information of each point in the reconstructed point cloud according to the processed attribute information of the associated point set. When attribute recoloring is carried out, the method carries out merging processing on the repeated points in the original point cloud, reduces the influence of the repeated points on the reconstructed attribute value, and improves the quality and the coding performance of the reconstructed point cloud while not increasing the coding complexity.
Description
Technical Field
The invention belongs to the technical field of point cloud coding, and particularly relates to a point cloud attribute recoloring method, a point cloud attribute recoloring device and a point cloud attribute recoloring encoder.
Background
The point cloud is a set of randomly distributed discrete points in space that represent the spatial structure and surface attributes of a three-dimensional object or scene. Generally, the three-dimensional point cloud data includes geometric information representing three-dimensional space coordinates of each point, and attribute information such as color, reflectivity and the like attached to each point, and may have material or other information according to different application scenarios.
With the continuous development of point cloud technology, the compression and encoding of point cloud data becomes an important research problem. At present, the Standard working Group (AVS) of the domestic digital Audio and Video coding Standard (Standard) of China and the Moving Picture Experts Group (MPEG) in the International organization for standardization both make the Standard of point cloud coding. For the original point cloud, whether the platform of AVS or the platform of G-PCC, the geometric information and the attribute information of the point cloud are coded and decoded separately. At this stage, attribute encoding is mainly performed for color information and reflectivity information. For the existing AVS platform and G-PCC platform, when performing attribute coding, it is usually necessary to convert the color information in the original point cloud attribute from RGB color space to luminance and chrominance color space; and then, recoloring the point cloud by using the reconstructed geometric information so that the uncoded attribute information corresponds to the reconstructed geometric information, which is called a point cloud attribute recoloring process. And then the following attribute prediction and encoding processes are performed.
However, since the point cloud sequence itself has repeated points, neither the existing AVS platform nor the existing G-PCC platform considers repeated points of the point cloud itself when performing geometric lossy re-coloring, that is, the calculation of the reconstruction attribute value may calculate repeated points of the point cloud itself many times, which affects the reconstruction attribute value, thereby affecting the new performance of encoding.
Disclosure of Invention
In order to solve the above problems in the prior art, the invention provides a point cloud attribute recoloring method, a point cloud attribute recoloring device and an encoder. The technical problem to be solved by the invention is realized by the following technical scheme:
a point cloud attribute recoloring method, comprising:
acquiring and processing original point cloud to obtain reconstructed point cloud;
finding out associated points corresponding to each point in the reconstructed point cloud from the original point cloud to obtain an associated point set;
combining repeated points of the original point cloud in the associated point set based on the original attribute information of all the points in the associated point set to obtain the attribute information of the processed associated point set;
and calculating the attribute information of each point in the reconstructed point cloud according to the processed attribute information of the associated point set.
In an embodiment of the present invention, finding out an associated point corresponding to each point in the reconstructed point cloud from the original point cloud to obtain an associated point set, including:
for each point in the first reconstructed point cloud
Finding a plurality of points P quantized to the point in the original point cloudk(i) As a set of association points, note:
U(i)＝(P1k(i))k∈{1，...，D1(i)}；
wherein D1(i) is the number of points of U (i), P1k(i)∈(Pi)i＝0...N-1，PiRepresenting points in the original point cloud.
In an embodiment of the present invention, merging repeated points of the original point cloud in the associated point set based on the original attribute information of all the points in the associated point set to obtain the attribute information of the processed associated point set, includes:
merging each group of repeated points of the original point cloud in the associated point set into one point respectively, and obtaining an attribute value of the merged point according to the original attribute value of each group of repeated points; wherein, the attribute value of the merging point is the mean value, or the median, or the mode, or the first value, or the maximum value, or the minimum value of the original attribute values of all the repeated points in the group;
and obtaining the attribute information of the processed associated point set based on the original attribute value of the non-repeated point of the original point cloud in the associated point set and the attribute value of the merging point.
In an embodiment of the present invention, a calculation formula of the attribute information of each point in the first reconstructed point cloud is as follows:
wherein the content of the first and second substances,
representing first reconstructed point cloud midpoint
Property value of, A1k(i) Representing the set of associated points U (i) midpoint P1k(i) And point P1k(i) Representing one point or a combination of points, wk(i) Indicating point P1k(i) The weighted weight values of (1).
In an embodiment of the present invention, finding out an associated point corresponding to each point in the reconstructed point cloud from the original point cloud to obtain an associated point set, further includes:
for each point in the second reconstructed point cloud
Finding a plurality of points closest to the point in the original point cloud, and recording as:
W(i)＝(Pk *(i))k∈{1，...，C(i)}；
wherein, Pk *(i) Is and
points with equal distance, C (i) is the number of points W (i);
for each point (P) in the original point cloudi)i＝0...N-1Finding a point closest to the second reconstruction point cloud in the second reconstruction point cloud;
for each point in the second reconstructed point cloud
The original point cloud is processedDot
The set of points v (i) as the closest points is set of associated points, denoted as:
V(i)＝(P2k(i))k∈{1，...，D2(i)}；
wherein D2(i) is the number of points of V (i), P2k(i)∈(Pi)i＝0...N-1，PiRepresenting points in the original point cloud.
In an embodiment of the present invention, a calculation formula of the attribute information of each point in the second reconstruction point cloud is:
if V (i) is empty, point
The reconstructed property values of (a) are:
if V (i) is not empty, then point
The reconstructed property values of (a) are:
wherein A isk *(i) Representing the midpoint P of the original point cloudk *(i) Property value of, A2k(i) Representing the midpoint P2 in the set of associated points V (i)k(i) And point P2k(i) Representing a point or a point at which a plurality of points merge.
In an embodiment of the present invention, finding out an associated point corresponding to each point in the reconstructed point cloud from the original point cloud to obtain an associated point set, further includes:
for each point in the third reconstructed point cloud
Finding a point P closest to the point in the original point cloudi *Wherein point Pi *The corresponding attribute value is Ai *；
For each point (P) in the original point cloudi)i＝0...N-1Finding a point closest to the third reconstructed point cloud;
for each point in the third reconstructed point cloud
The point is added to the original point cloud
The set V' (i) of points as the closest points is set as a related point:
V′(i)＝(P3k(i))k∈{1，...，D3(i)}；
wherein D3(i) is the number of points of V' (i), P3k(i)∈(Pi)i＝0...N-1，PiRepresenting points in the original point cloud.
In an embodiment of the present invention, a calculation formula of the attribute information of each point in the third reconstructed point cloud is:
if V' (i) is empty, then
The reconstructed property values of (a) are:
if V' (i) is not empty, then
The reconstructed property values of (a) are:
wherein, A3k(i) Represents the point P3 in the associated point set V' (i)k(i) And point P3k(i) Representing a point or a point at which a plurality of points merge.
Another embodiment of the present invention provides a point cloud attribute recoloring apparatus, including:
the data acquisition module is used for acquiring and processing the original point cloud to obtain a reconstructed point cloud;
the correlation point set building module is used for finding out correlation points corresponding to each point in the reconstructed point cloud from the original point cloud to obtain a correlation point set;
the repeated point processing module is used for merging repeated points of the original point cloud in the associated point set based on the original attribute information of all the points in the associated point set to obtain the attribute information of the processed associated point set;
and the attribute calculation module is used for calculating the attribute information of each point in the reconstructed point cloud according to the processed attribute information of the associated point set.
One embodiment of the invention provides a point cloud encoder, which comprises a geometric coding system and an attribute coding system, wherein the attribute coding system comprises the point cloud attribute recoloring device according to the embodiment.
Compared with the prior art, the invention has the beneficial effects that:
according to the point cloud attribute re-coloring method, the influence of the repeat point of the point cloud on the reconstructed attribute value during geometric quantization is considered, the repeat point in the original point cloud is merged during attribute re-coloring, the influence of the repeat point on the reconstructed attribute value is reduced, and the attribute reconstruction performance is improved while the coding complexity is not increased.
Drawings
Fig. 1 is a schematic flow chart of a point cloud attribute recoloring method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a point cloud attribute recoloring apparatus according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a point cloud encoder according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to specific examples, but the embodiments of the present invention are not limited thereto.
Example one
Referring to fig. 1, fig. 1 is a schematic flow chart of a point cloud attribute recoloring method according to an embodiment of the present invention, including:
acquiring and processing original point cloud to obtain reconstructed point cloud;
finding out associated points corresponding to each point in the reconstructed point cloud from the original point cloud to obtain an associated point set;
combining repeated points of the original point cloud in the associated point set based on the original attribute information of all the points in the associated point set to obtain the attribute information of the processed associated point set;
and calculating the attribute information of each point in the reconstructed point cloud according to the processed attribute information of the associated point set.
In practical applications, the reconstructed point cloud may be obtained in various ways, for example, in an AVS platform, the reconstructed point cloud may be obtained through coordinate translation quantization and then subjected to a re-coloring process, or after geometric reconstruction, the reconstructed point cloud information may be obtained and then subjected to a quantization process. In the G-PCC platform, the point cloud data obtained after geometric reconstruction is also subjected to quantization processing.
The embodiment provides a point cloud attribute recoloring method based on repeat point combination aiming at the three types of reconstructed point clouds and comprehensively considering the influence of the repeat points of the point clouds on the reconstructed attribute values during geometric quantization. When the method provided by the embodiment is used for attribute recoloring, the repeated points in the original point cloud are combined, so that the influence of the repeated points on the reconstructed attribute value is reduced, and the quality and the coding performance of the reconstructed point cloud are improved while the coding complexity is not increased.
Example two
The point cloud attribute recoloring method provided in the first embodiment is described in detail below by using an AVS platform.
In the current AVS platform, under geometrically lossy conditions, there are two recoloring methods. One is the fast recoloring method, which is performed after coordinate translation quantization. In this embodiment, the reconstructed point cloud obtained after coordinate translation quantization is referred to as a first reconstructed point cloud. The method specifically comprises the following steps:
the method comprises the following steps: and acquiring and processing the original point cloud to obtain a first reconstructed point cloud.
Specifically, in the point cloud AVS encoder framework, the geometric information of the original point cloud data is first coordinate-converted so that the point clouds are all contained in one bounding box. And then, quantizing to obtain a first reconstructed point cloud. The quantization of this step mainly plays a role of scaling, and since the quantization rounding makes the geometric information of a part of points the same, specifically, whether to remove the repeated points can be determined according to the parameters.
Let the geometric information of the original point cloud and the first reconstructed point cloud be respectively expressed as (P)i)i＝0...N-1And
wherein N and NrecPoints in the original point cloud and the first reconstructed point cloud are represented respectively. N if the repeat points resulting from quantization are removedrec＜N。
Step two: and finding out the associated points corresponding to each point in the first reconstructed point cloud from the original point cloud to obtain an associated point set.
For the fast recoloring method, because the point cloud sequence itself has repeat points, when recoloring is performed due to geometric damage, only repeat points obtained by quantification are considered, and repeat points of the point cloud itself are not considered, that is, the repeat points of the point cloud itself can be calculated for many times by calculating the reconstruction attribute value, thereby affecting the reconstruction attribute value. For example: points a and B are quantized to point C, where point B has several tens of repeat points, and the calculated attribute value of point C is affected by these repeat points, so that the result is closer to the attribute value of point B.
Therefore, when performing fast recoloring, the embodiment first performs merging processing on the repeated points in the original point cloud to reduce the influence of the repeated points on attribute calculation.
Specifically, the second step comprises:
for each point in the first reconstructed point cloud
Finding a plurality of points quantized to the point in the original point cloud as a related point set, and recording as:
U(i)＝(P1k(i))k∈{1，...，D1(i)}；
wherein D1(i) is the number of points of U (i), P1k(i)∈(Pi)i＝0...N-1，PiRepresenting points in the original point cloud.
Step three: and merging the repeated points of the original point cloud in the associated point set based on the original attribute information of all the points in the associated point set to obtain the attribute information of the processed associated point set.
Firstly, each point in the associated point set obtained in the second step carries the original attribute information of the point, but the point set has the repeat point of the original point cloud. Therefore, it is necessary to deal with these repetition points.
Specifically, each group of repeat points of the original point cloud in the associated point set are respectively merged into one point, and the attribute value of the merged point is obtained according to the original attribute value of each group of repeat points.
More specifically, the average, or the median, or the mode, or the first value, or the maximum value, or the minimum value of the original attribute values of all the repetition points of the group may be used as the attribute value of the merging point.
Preferably, the original attribute mean of the set of repeated points may be used as the attribute value of the merged point.
And then, obtaining attribute information of the associated point set based on the original attribute values of the non-repeated points of the original point cloud in the associated point set and the attribute values of the merging points.
Step four: and calculating the attribute information of each point in the first reconstructed point cloud according to the processed attribute information of the associated point set.
In this embodiment, the calculation formula of the attribute information of each point in the first reconstructed point cloud is as follows:
wherein the content of the first and second substances,
representing first reconstructed point cloud midpoint
Property value of, A1k(i) Representing the set of associated points U (i) midpoint P1k(i) And point P1k(i) Representing one point or a combination of points, wk(i) Indicating point P1k(i) The weighted weight values of (1).
Further, wk(i) The calculation formula of (2) is as follows:
wherein the content of the first and second substances,
representing a point of relevance set P1k(i) Divided by the quantization step size, ∈ is a constant whose value takes 0.1.
And obtaining attribute information of all points in the first reconstructed point cloud, and finishing point cloud attribute recoloring processing.
EXAMPLE III
The embodiment provides another point cloud recoloring method for an AVS platform, which is performed after geometric reconstruction, and the point cloud data after the geometric reconstruction of the AVS platform is referred to as a second reconstructed point cloud.
The method specifically comprises the following steps:
step 1: and acquiring and processing the original point cloud to obtain a second reconstruction point cloud.
Specifically, the geometric information of the original point cloud and the second reconstruction point cloud (point cloud after geometric reconstruction) are respectively expressed as (P)i)i＝0...N-1And
wherein N and NrecThe number of points in the original point cloud and the second reconstructed point cloud are respectively represented. N if the repeated points generated by geometric quantization in the geometric reconstruction process are removedrec＜N。
Step 2: and finding out the associated points corresponding to each point in the second reconstructed point cloud from the original point cloud to obtain an associated point set.
In this embodiment, an associated point set corresponding to each point in the second reconstructed point cloud is obtained mainly by using a nearest neighbor lookup method, and specifically, the method is implemented by using a KDTree data structure.
Specifically, step 2 comprises:
21) for each point in the second reconstructed point cloud
Finding a plurality of points closest to the point in the original point cloud, and recording as:
W(i)＝(Pk *(i))k∈{1，...，C(i)}；
wherein, Pk *(i) Is and
points with equal distance, C (i) is the number of points W (i); w (i) may comprise one or more points.
22) For each point (P) in the original point cloudi)i＝0...N-1Finding a point closest to the point in the reconstructed point cloud;
23) for each point in the reconstructed point cloud
The point is added to the original point cloud
The set of points v (i) as the closest points is set of associated points, denoted as:
V(i)＝(P2k(i))k∈{1，...，D2(i)}；
wherein D2(i) is the number of points of V (i), P2k(i)∈(Pi)i＝0...N-1，PiRepresenting a point in the original point cloud, v (i) may be empty, or may include one or more points.
And step 3: and merging the repeated points of the original point cloud in the associated point set based on the original attribute information of all the points in the associated point set to obtain the attribute information of the processed associated point set.
Like the first embodiment, all the duplicate points in each group may be merged into one point, the original attribute mean, or the median, or the mode of all the duplicate points in the group may be used as the attribute value of the merged point, and the original attribute value of the non-duplicate point is retained, so as to obtain the attribute information of all the points in the associated point set.
And 4, step 4: and calculating the attribute information of each point in the second reconstruction point cloud according to the processed attribute information of the associated point set.
Specifically, the calculation formula of the attribute information of each point in the second reconstruction point cloud is as follows:
if V (i) is empty, point
The reconstructed property values of (a) are:
if V (i) is not empty, then point
The reconstructed property values of (a) are:
wherein A isk *(i) Representing the midpoint P of the original point cloudk *(i) Property value of, A2k(i) Representing the midpoint P2 in the set of associated points V (i)k(i) And point P2k(i) Representing a point or a point at which a plurality of points merge.
And obtaining attribute information of all points in the second reconstructed point cloud, and finishing point cloud attribute recoloring treatment.
EXAMPLE III
In this embodiment, a point cloud attribute recoloring method provided in the first embodiment above is described in detail by using a G-PCC platform.
In current MPEG G-PCC, under geometrically lossy conditions, recoloring after geometric reconstruction is required, where calculations involving nearest neighbor lookups also use KDTree data structures. In this embodiment, the point cloud data after the geometric reconstruction of the G-PCC platform is referred to as a third reconstructed point cloud.
Step A: and acquiring and processing the original point cloud to obtain a third reconstructed point cloud.
Specifically, the geometric information of the original point cloud and the third reconstructed point cloud (point cloud after geometric reconstruction) are respectively expressed as (P)i)i＝0...N-1And
wherein N and NrecThe number of points in the original point cloud and the third reconstructed point cloud are respectively represented. N if the repeated points generated by geometric quantization in the geometric reconstruction process are removedrec＜N。
And B: finding out the associated points corresponding to each point in the third reconstructed point cloud from the original point cloud to obtain an associated point set, which specifically comprises the following steps:
B1) for each point in the third reconstructed point cloud
Finding the point P nearest to the point in the original point cloudi *Wherein point Pi *The corresponding attribute value is Ai *；
B2) For each point (P) in the original point cloudi)i＝0...N-1Finding a point closest to the point in the reconstructed point cloud;
B3) for each point in the reconstructed point cloud
The point is added to the original point cloud
The set V' (i) of points as the closest points is set as a related point:
V′(i)＝(P3k(i))k∈{1，...，D3(i)}；
wherein D3(i) is the number of points of V' (i), P3k(i)∈(Pi)i＝0...N-1，PiRepresenting a point in the original point cloud, V' (i) may be empty or may include one or more points.
And C: and merging the repeated points of the original point cloud in the associated point set based on the original attribute information of all the points in the associated point set to obtain the attribute information of the processed associated point set.
Like the first and second embodiments, all the repeated points in each group may be merged into one point, the original attribute mean, or the median, or the mode of all the repeated points in the group may be used as the attribute value of the merged point, and the original attribute value of the non-repeated point is retained, so as to obtain the attribute information of all the points in the associated point set.
Step D: and calculating the attribute information of each point in the third reconstructed point cloud according to the processed attribute information of the associated point set.
Specifically, the calculation formula of the attribute information of each point in the third reconstructed point cloud is as follows:
if V' (i) is empty, then
The reconstructed property values of (a) are:
if V' (i) is not empty, then
The reconstructed property values of (a) are:
wherein, A3k(i) Represents the point P3 in the associated point set V' (i)k(i) And point P3k(i) Representing a point or a point at which a plurality of points merge.
And obtaining attribute information of all points in the third reconstructed point cloud, and finishing point cloud attribute recoloring processing.
Example four
On the basis of the first embodiment, the present embodiment provides a point cloud attribute recoloring device, please refer to fig. 2, and fig. 2 is a schematic structural diagram of the point cloud attribute recoloring device according to the first embodiment of the present invention, which includes:
the data acquisition module 11 is used for acquiring and processing the original point cloud to obtain a reconstructed point cloud;
the associated point set constructing module 12 is configured to find an associated point corresponding to each point in the reconstructed point cloud from the original point cloud to obtain an associated point set;
a repeated point processing module 13, configured to perform merging processing on repeated points of the original point cloud in the associated point set based on the original attribute information of all the points in the associated point set, so as to obtain attribute information of the processed associated point set;
and the attribute calculation module 14 is configured to calculate attribute information of each point in the reconstructed point cloud according to the processed attribute information of the associated point set.
The point cloud attribute recoloring device provided in this embodiment can implement the point cloud attribute recoloring method described in the first embodiment, and may be applied to an AVS platform and a G-PCC platform.
EXAMPLE five
On the basis of the fourth embodiment, the present embodiment provides a point cloud encoder, please refer to fig. 3, and fig. 3 is a schematic structural diagram of a point cloud reconstruction system according to an embodiment of the present invention, which includes a geometric encoding system and an attribute encoding system, where the attribute encoding system includes the point cloud attribute recoloring device according to the fourth embodiment.
When the point cloud encoder provided by the embodiment performs attribute encoding, the influence of the repeat point of the point cloud on the reconstructed attribute value during geometric quantization is considered, the repeat point of the point cloud itself is merged in the recoloring process, the influence of the repeat point on the reconstructed attribute value is reduced, and the quality and the encoding performance of the reconstructed point cloud are improved while the encoding complexity is not increased.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (10)
1. A point cloud attribute recoloring method, comprising:
acquiring and processing original point cloud to obtain reconstructed point cloud;
finding out associated points corresponding to each point in the reconstructed point cloud from the original point cloud to obtain an associated point set;
combining repeated points of the original point cloud in the associated point set based on the original attribute information of all the points in the associated point set to obtain the attribute information of the processed associated point set;
and calculating the attribute information of each point in the reconstructed point cloud according to the processed attribute information of the associated point set.
2. The method of re-coloring point cloud attributes according to claim 1, wherein finding associated points in the original point cloud corresponding to each point in the reconstructed point cloud, resulting in a set of associated points, comprises:
for each point in the first reconstructed point cloud
Finding a plurality of points P quantized to the point in the original point cloudk(i) As a set of association points, note:
U(i)＝(P1k(i))k∈{1，...，D1(i)}；
wherein D1(i) is the number of points of U (i), P1k(i)∈(Pi)i＝0...N-1，PiRepresenting points in the original point cloud.
3. The point cloud attribute recoloring method of claim 1, wherein merging repeated points of the original point cloud in the associated point set based on original attribute information of all the points in the associated point set to obtain attribute information of the processed associated point set comprises:
merging each group of repeated points of the original point cloud in the associated point set into one point respectively, and obtaining an attribute value of the merged point according to the original attribute value of each group of repeated points; wherein, the attribute value of the merging point is the mean value, or the median, or the mode, or the first value, or the maximum value, or the minimum value of the original attribute values of all the repeated points in the group;
and obtaining the attribute information of the processed associated point set based on the original attribute value of the non-repeated point of the original point cloud in the associated point set and the attribute value of the merging point.
4. The point cloud attribute recoloring method of claim 2, wherein the formula for calculating the attribute information of each point in the first reconstructed point cloud is:
wherein the content of the first and second substances,
representing first reconstructed point cloud midpoint
Property value of, A1k(i) Representing the set of associated points U (i) midpoint P1k(i) And point P1k(i) Representing one point or a combination of points, wk(i) Indicating point P1k(i) The weighted weight values of (1).
5. The method of re-coloring point cloud attributes according to claim 1, wherein finding out associated points corresponding to each point in the reconstructed point cloud in the original point cloud to obtain an associated point set, further comprises:
for each point in the second reconstructed point cloud
Finding a plurality of points closest to the point in the original point cloud, and recording as:
W(i)＝(Pk *(i))k∈{1，...，C(i)}；
wherein, Pk *(i) Is and
points with equal distance, C (i) is the number of points W (i);
for each point (P) in the original point cloudi)i＝0...N-1Finding a point closest to the second reconstruction point cloud in the second reconstruction point cloud;
for each point in the second reconstructed point cloud
The point is added to the original point cloud
The set of points v (i) as the closest points is set of associated points, denoted as:
V(i)＝(P2k(i))k∈{1，...，D2(i)}；
wherein D2(i) is the number of points of V (i), P2k(i)∈(Pi)i＝0...N-1，PiRepresenting points in the original point cloud.
6. The point cloud attribute recoloring method of claim 5, wherein the formula for calculating the attribute information of each point in the second reconstructed point cloud is:
if V (i) is empty, point
The reconstructed property values of (a) are:
if V (i) is not empty, then point
The reconstructed property values of (a) are:
wherein A isk *(i) Representing the midpoint P of the original point cloudk *(i) Property value of, A2k(i) Representing the midpoint P2 in the set of associated points V (i)k(i) And point P2k(i) Representing a point or a point at which a plurality of points merge.
7. The method of re-coloring point cloud attributes according to claim 1, wherein finding out associated points corresponding to each point in the reconstructed point cloud in the original point cloud to obtain an associated point set, further comprises:
for each point in the third reconstructed point cloud
Finding a point P closest to the point in the original point cloudi *Wherein point Pi *The corresponding attribute value is Ai *；
For each point (P) in the original point cloudi)i＝0...N-1Finding a point closest to the third reconstructed point cloud;
for each point in the third reconstructed point cloud
The point is added to the original point cloud
The set V' (i) of points as the closest points is set as a related point:
V′(i)＝(P3k(i))k∈{1，...，D3(i)}；
wherein D3(i) is the number of points of V' (i), P3k(i)∈(Pi)i＝0...N-1，PiRepresenting points in the original point cloud.
8. The point cloud attribute recoloring method of claim 7, wherein the formula for calculating the attribute information of each point in the third reconstructed point cloud is:
if V' (i) is empty, then
The reconstructed property values of (a) are:
if V' (i) is not empty, then
The reconstructed property values of (a) are:
wherein, A3k(i) Represents the point P3 in the associated point set V' (i)k(i) And point P3k(i) Representing a point or a point at which a plurality of points merge.
9. A point cloud attribute recoloring apparatus, comprising:
the data acquisition module (11) is used for acquiring and processing the original point cloud to obtain a reconstructed point cloud;
the association point set building module (12) is used for finding out association points corresponding to each point in the reconstructed point cloud from the original point cloud to obtain an association point set;
a repeated point processing module (13) for merging repeated points of the original point cloud in the associated point set based on the original attribute information of all the points in the associated point set to obtain the attribute information of the processed associated point set;
and the attribute calculation module (14) is used for calculating the attribute information of each point in the reconstructed point cloud according to the processed attribute information of the associated point set.
10. A point cloud encoder comprising a geometric encoding system and an attribute encoding system, wherein the attribute encoding system comprises the point cloud attribute recoloring apparatus of claim 9.
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