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Normal vector-based point cloud attribute compression method, apparatus and device, and medium

A compression method and normal vector technology, applied in image data processing, instruments, calculations, etc., can solve problems such as large errors in compression results, and achieve the effect of reducing errors

Active Publication Date: 2021-07-09
SUN YAT SEN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Both of these methods will cause large errors in the compression results.

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  • Normal vector-based point cloud attribute compression method, apparatus and device, and medium
  • Normal vector-based point cloud attribute compression method, apparatus and device, and medium
  • Normal vector-based point cloud attribute compression method, apparatus and device, and medium

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Embodiment Construction

[0065] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application.

[0066] Aiming at the problems existing in the prior art, an embodiment of the present invention provides a method for compressing point cloud attributes based on normal vectors, including:

[0067] Calculate the geometric information of the point cloud data to be compressed;

[0068] Performing Morton code sorting according to the geometric information of the point cloud data to obtain the Morton sequence;

[0069] Obtaining the target prediction point of the current coding point according to the Morton order traversal, and determining the prediction weight of the target ...

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Abstract

The invention discloses a point cloud attribute compression method and device based on a normal vector, equipment and a medium. The method comprises the following steps: calculating geometric information of to-be-compressed point cloud data; performing Morton code sorting according to the geometric information of the point cloud data to obtain a Morton sequence; traversing according to the Morton sequence to obtain a target prediction point of the current coding point, and determining a prediction weight of the target prediction point; constructing a k-d tree; searching a nearest neighbor point of the current coding point and the target prediction point according to the k-d tree; performing plane fitting according to the nearest neighbor point to obtain a normal vector of the plane; correcting the prediction weight according to the normal vector to obtain a target weight; and determining a coding result of the point cloud data according to the target weight. The method can reduce the error of the point cloud attribute compression result, and can be widely applied to the technical field of point cloud data processing.

Description

technical field [0001] The invention relates to the technical field of point cloud data processing, in particular to a method, device, equipment and medium for compressing point cloud attributes based on normal vectors. Background technique [0002] A point cloud is a form of representation of a three-dimensional object or scene, which is composed of a set of discrete points that are randomly distributed in space and express the spatial structure and surface properties of a three-dimensional object or scene. In addition to the geometric coordinates, the points in the point cloud also include some additional attributes, such as color, reflectivity, etc. There is no specified spatial connection or sequential relationship between the points. Point cloud data is mainly divided into three categories according to the way of acquisition: 1. Static point cloud: the object is static, and the equipment for obtaining point cloud is also static. 2. Dynamic point cloud: The object is m...

Claims

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Application Information

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IPC IPC(8): G06T9/00
CPCG06T9/00
Inventor 梁凡刘一晴
Owner SUN YAT SEN UNIV