Visual task-based point cloud data compression quality evaluation method and system

Abstract

The invention provides a visual task-based point cloud data compression quality evaluation method and system. The method comprises the steps of inputting original three-dimensional point cloud data; processing the original three-dimensional point cloud data, and calculating a first evaluation index of a visual task according to the processed first data; encoding and decoding the original three-dimensional point cloud data, processing the encoded and decoded three-dimensional point cloud data, and calculating a second evaluation index of the visual task according to the processed second data; and comparing the first evaluation index with the second evaluation index to obtain an evaluation index comparison result, and evaluating the point cloud data compression quality according to the comparison result. Compared with an existing point cloud quality evaluation method, the invention has pertinence, and the point cloud compression quality can be measured according to specific visual tasks.In addition, the point cloud compression quality can be comprehensively measured according to various visual tasks.

Description

technical field [0001] The invention relates to the field of point cloud data processing, in particular to a method and system for evaluating the quality of point cloud data compression based on visual tasks. Background technique [0002] Due to the rapid development of 3D scanning technology and acquisition equipment, the point cloud data obtained by measuring 3D models is becoming more and more accurate and huge. The point cloud data is composed of three-dimensional coordinate information, texture information, depth information, etc. of the object after three-dimensional scanning. At present, general laser scanning equipment can easily obtain hundreds of thousands or even millions of point cloud data from the surface of objects. Massive point cloud data has brought a huge burden to computer storage, processing and transmission. [0003] With the sharp increase in the processing complexity of the grid model, the advantages of the point cloud model are more obvious. The p...

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Problems solved by technology

[0004] The 3D point cloud model can express the shape of the object more accurately, but its data volume is huge

[0005] However, since the current research on point cloud models is still in its infancy, there are few studies on the quality evaluation of point cloud models.

In addition, the vast majority of existing point cloud quality evaluation methods evaluate the quality of point clouds based on the visual effects presented by point clouds, and do not guide them based on specific visual tasks (such as point cloud classification, segmentation, recognition, etc.) and specific visual task evaluation indicators. Evaluation results of point cloud compression quality

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