US 12,755,436 B2Granted

Point cloud attribute prediction method and apparatus, terminal, and storage medium

AssigneePeng Cheng Laboratory

InventorsJing Wang, Qi Zhang, Yueru Chen, Ge Li, Wen Gao

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Abstract

A point cloud attribute prediction method and apparatus, a terminal and a storage medium are disclosed. The method includes obtaining target neighbor points corresponding to a target point by a first spatial distance, and determining an optimization weight corresponding to each target neighbor point respectively based on a second spatial distance, and finally determining an attribute prediction value corresponding to the target point based on each target neighbor point and optimization weight corresponding to each target neighbor point respectively. The present disclosure optimizes the weight corresponding to each target neighbor point respectively based on the spatial distance, which can improve the correlation between the geometry information and the attribute information of the point cloud, provide more accurate prediction values when performing point cloud attribute prediction, and thus improve the encoding and decoding performance of the point cloud attributes.

Claims14

Dependent claims

A point cloud attribute prediction method, comprising:

obtaining several target neighbor points by selecting points in a point cloud based on first spatial distances between the points in the point cloud and a target point;
determining optimization weights of the target neighbor points based on second spatial distances between the target neighbor points and the target point;
and
determining an attribute prediction value of the target point based on the target neighbor points and the optimization weights of the target neighbor points;
wherein determining optimization weights of the target neighbor points based on second spatial distances between the target neighbor points and the target point, comprises at least one of the following:
determining optimization distances of the target neighbor points as products of the second spatial distances and the number of target neighbor points with the same second spatial distance;
determining the optimization weights of the target neighbor points as inverses of the optimization distances of the target neighbor points;
or
when the number of target neighbor points with the same second spatial distance is greater than 1, determining optimization distances of the target neighbor points as quotients obtained by dividing products of the second spatial distances and the number of target neighbor points with the same second spatial distance by an attribute quantization step size;
determining the optimization weights of the target neighbor points as inverses of the optimization distances of the target neighbor points;
or
determining optimization coefficients as a minimum value of the number of target neighbor points with the same second spatial distance and an attribute quantization step size;
determining optimization distances of the target neighbor points as quotients obtained by dividing products of the second spatial distances and the number of target neighbor points with the same second spatial distance by the optimization coefficients;
determining the optimization weights of the target neighbor points as inverses of the optimization distances of the target neighbor points.

11 more elements

A terminal, wherein the terminal comprises a memory and one or more processors;

the memory stores one or more programs, which when executed by the one or more processors, cause the one or more processors to perform a point cloud attribute prediction method, comprising:
obtaining several target neighbor points by selecting points in a point cloud based on first spatial distances between the points in the point cloud and a target point;
determining optimization weights of the target neighbor points based on second spatial distances between the target neighbor points and the target point;
and
determining an attribute prediction value of the target point based on the target neighbor points and the optimization weights of the target neighbor points;
wherein determining optimization weights of the target neighbor points based on second spatial distances between the target neighbor points and the target point, comprises at least one of the following:
determining optimization distances of the target neighbor points as products of the second spatial distances and the number of target neighbor points with the same second spatial distance;
determining the optimization weights of the target neighbor points as inverses of the optimization distances of the target neighbor points;
or
when the number of target neighbor points with the same second spatial distance is greater than 1, determining optimization distances of the target neighbor points as quotients obtained by dividing products of the second spatial distances and the number of target neighbor points with the same second spatial distance by an attribute quantization step size;
determining the optimization weights of the target neighbor points as inverses of the optimization distances of the target neighbor points;
or
determining optimization coefficients as a minimum value of the number of target neighbor points with the same second spatial distance and an attribute quantization step size;
determining optimization distances of the target neighbor points as quotients obtained by dividing products of the second spatial distances and the number of target neighbor points with the same second spatial distance by the optimization coefficients;
determining the optimization weights of the target neighbor points as inverses of the optimization distances of the target neighbor points.

12 more elements

A non-transitory computer-readable storage medium, storing instructions, which when executed by one or more processors, cause the one or more processors to perform a point cloud attribute prediction method, comprising:

obtaining several target neighbor points by selecting points in a point cloud based on first spatial distances between the points in the point cloud and a target point;
determining optimization weights of the target neighbor points based on second spatial distances between the target neighbor points and the target point;
and
determining an attribute prediction value of the target point based on the target neighbor points and the optimization weights of the target neighbor points;
wherein determining optimization weights of the target neighbor points based on second spatial distances between the target neighbor points and the target point, comprises at least one of the following:
determining optimization distances of the target neighbor points as products of the second spatial distances and the number of target neighbor points with the same second spatial distance;
determining the optimization weights of the target neighbor points as inverses of the optimization distances of the target neighbor points;
or
when the number of target neighbor points with the same second spatial distance is greater than 1, determining optimization distances of the target neighbor points as quotients obtained by dividing products of the second spatial distances and the number of target neighbor points with the same second spatial distance by an attribute quantization step size;
determining the optimization weights of the target neighbor points as inverses of the optimization distances of the target neighbor points;
or
determining optimization coefficients as a minimum value of the number of target neighbor points with the same second spatial distance and an attribute quantization step size;
determining optimization distances of the target neighbor points as quotients obtained by dividing products of the second spatial distances and the number of target neighbor points with the same second spatial distance by the optimization coefficients;
determining the optimization weights of the target neighbor points as inverses of the optimization distances of the target neighbor points.

11 more elements

Dependencies are inferred from the claim text.