Object Surface Reconstruction Method in 3D Image

Abstract

The target body surface reconstruction method in the three-dimensional image disclosed by the present invention includes the following steps: S1: Obtain the target body shell data by using the point density analysis method and the improved grid division method, that is, the depth-first traversal method; S2: Obtain the target body shell data obtained in S1 The shell data of the object is regarded as the point cloud in computer graphics, and the surface reconstruction of the object body is carried out by using the energy function method based on image segmentation. The target body surface reconstruction method in the three-dimensional image proposed by the present invention first adopts the improved image segmentation technology to process the volume data of the obtained target body, that is, the seismic sampling point data, so that it can be applied to the point data in computer graphics. Cloud surface reconstruction method, and then use the improved energy function method based on image segmentation to reconstruct the surface of the target body, which overcomes the problem of surface reconstruction of the three-dimensional target body whose sampling points are volume data, and is suitable for large noise content or flat shape, Surface reconstruction of undersampled objects with inconsistent sampling in all directions.

Description

technical field [0001] The invention belongs to the technical field of three-dimensional images, and in particular relates to a surface reconstruction method of a target body in a three-dimensional image of seismic data. Background technique [0002] The surface reconstruction of the target body in the 3D image is widely used in scientific research and production fields such as geophysics, geological exploration, medical and health care, and remote sensing and telemetry. For example, in geological interpretation work, the identification, tracking and modeling of subsurface objects and stratigraphic structures are very important; for example, in medical and health work, 3D modeling and surface reconstruction of internal tissues and organs of the human body are very important. Construction is also a very important process. [0003] For the surface reconstruction of the 3D target body whose sampling points are the surface points of the target body, the point cloud surface reco...

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Problems solved by technology

However, since the 3D image data is volume data, the surface reconstruction method based on the point cloud cannot be directly used for the surface reconstruction of the target body in the 3D image.

[0004] For the surface reconstruction of the 3D target body whose sampling points are volume data, the current main solutions can be divided into two categories. One is based on the image segmentation technology in image processing, which is used to segment the 3D image and separate the target body; One is to use volume rendering, volume perspective, point and other scalar forms to directly visualize three-dimensional volume data, such as the commercial software petrel, which expresses the target volume in the form of volume perspective or point rendering, but these methods are not very good Reconstruct the surface of the target body, especially the seismic target body in the geological field. This is because the surface reconstruction of the 3D image target body is very difficult, mainly manifested in:

[0005] (1) The 3D image data is the volume sampling point in the scene where the target body is located, and the original data reconstructed based on the point cloud surface is the sampling point on the surface of the target body, so the point cloud surface reconstruction method in computer graphics cannot be used directly

[0006] (2) Especially for seismic data in the geological field, the shape of the target body in the seismic image is irregular and unpredictable, and it is impossible to make any geometric constraints on the outline of the target body in advance

[0007] (3) The target body in the seismic image has the characteristics of ambiguous boundaries and low signal-to-noise ratio of seismic data, which makes surface reconstruction more difficult

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