A saliency feature enhanced sampling method based on octree-like indexes

A fork tree and index technology, applied in the field of saliency feature enhancement sampling based on octree-like index, can solve the problems of poor skeleton repeatability and insufficient skeleton accuracy.

Active Publication Date: 2019-02-12
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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Problems solved by technology

[0011] In order to solve the above technical problems, the main purpose of the present invention is to provide a method for managing and operating point clouds efficiently and conveniently, and to better represent the model Local features, to solve the problems of insufficient accuracy of the skeleton caused by the lack of point cloud of the L1 algorithm and poor repeatability of the skeleton caused by random sampling

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  • A saliency feature enhanced sampling method based on octree-like indexes
  • A saliency feature enhanced sampling method based on octree-like indexes
  • A saliency feature enhanced sampling method based on octree-like indexes

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[0113] The technical solutions of the present invention will be further specifically described below through embodiments and in conjunction with the accompanying drawings.

[0114] The present invention proposes a salient feature enhanced sampling method based on octree-like index, the main purpose of which is to optimize the existing L1 skeleton extraction algorithm. This method realizes the segmentation and management of spatial point clouds by constructing an octave-like spatial index, and then adaptively enhances the salient features of point clouds, and samples the enhanced point clouds through a sampling strategy. Experimental results show that this method can not only ensure better repeatability and descriptiveness of the skeleton, but also improve the efficiency of the original algorithm. Its algorithm flow is as follows figure 1 shown.

[0115] The present invention operates on three-dimensional point clouds, so a good point cloud index is the basis of all subsequen...

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Abstract

A salient feature enhancement sampling method based on octree-like indexes is proposed. Firstly, based on the octree-like spatial segmentation, the adaptive point cloud feature enhancement is appliedto the model with serious local missing. Then, the subspace-based nearest neighbor sampling algorithm is used to downsample the enhanced point cloud. Finally, the skeleton is extracted from the enhanced point cloud according to the obtained sample points. The experimental results show that the octree-like space can realize the efficient management of the point cloud and improve the accuracy and timeliness of the skeleton. The adaptive point cloud enhanced sampling strategy makes the extracted skeleton more repeatable and descriptive. The optimized algorithm in the invention is applicable to point cloud models in various domains, and has good adaptability and robustness.

Description

technical field [0001] The invention relates to the field of multimedia data, in particular to a salient feature enhancement sampling method based on an octree-like index applied to a three-dimensional model. Background technique [0002] In recent years, 3D laser scanning technology has developed rapidly. With the continuous improvement of its performance in terms of efficiency, accuracy, ranging range, etc. and the development of related theories, 3D scanning technology has been applied in more and more fields. As a new type of multimedia data, 3D models have a good ability to express the real world, especially the rapid development of 3D data scanning equipment and computer hardware, which has boosted the vigorous development of this field. Three-dimensional laser scanning directly performs three-dimensional dense sampling on the earth's surface, and can quickly obtain massive and irregular spatially distributed three-dimensional point clouds with three-dimensional coordi...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T17/00G06T17/10G06T17/20
CPCG06T17/005G06T17/10G06T17/20
Inventor 鲁斌王强李阿楠陈娟
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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