Point cloud geometric lossy compression method based on voxel convolution

A compression method and plain volume technology, applied in image data processing, instruments, electrical components, etc., can solve problems such as low compression rate and unsatisfactory compression effect, and achieve the effect of reducing requirements

Inactive Publication Date: 2020-01-10
PLEX VR DIGITAL TECH CO LTD
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Problems solved by technology

The point cloud data collected by 3D scanning equipment is usually a scattered point cloud. There is no corresponding topological relationship between the points of the scattered point cloud, so it is difficult to process. The existing traditional co...

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  • Point cloud geometric lossy compression method based on voxel convolution
  • Point cloud geometric lossy compression method based on voxel convolution
  • Point cloud geometric lossy compression method based on voxel convolution

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Embodiment Construction

[0035] The following describes several preferred embodiments of the present invention with reference to the accompanying drawings, so as to make the technical content clearer and easier to understand. The present invention can be embodied in many different forms of embodiments, and the protection scope of the present invention is not limited to the embodiments mentioned herein.

[0036] In the drawings, components with the same structure are denoted by the same numerals, and components with similar structures or functions are denoted by similar numerals. The size and thickness of each component shown in the drawings are shown arbitrarily, and the present invention does not limit the size and thickness of each component. In order to make the illustration clearer, the thickness of parts is appropriately exaggerated in some places in the drawings.

[0037] As mentioned above, the current geometric compression of point cloud data is mainly based on traditional methods such as uni...

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Abstract

The invention discloses a point cloud geometric lossy compression method based on voxel convolution. Compression and decompression are carried out by carrying out convolution and deconvolution on voxels of a point cloud through a training model. Firstly, voxelization is carried out on point cloud data, a certain grid size is selected for voxels of point cloud to carry out 3D convolution operationto obtain feature data with smaller shapes and sizes, quantization processing is carried out on the feature data after convolution, uniform quantization noise is added during model training to improvethe generalization of the model, and the quantized data is compressed. During decompression, deconvolution is performed on the quantized feature data to obtain feature data of which the size is consistent with the shape and size of the initial point cloud voxel, normalizing the feature data, and judging whether each voxel unit is empty or not through a threshold value to obtain decompressed pointcloud data. During model training, focus loss is used as distortion loss to reduce the influence of too many voxel hollow samples on the model. According to the method, geometric compression can be efficiently carried out on the point cloud data, and the distortion rate after restoration is reduced.

Description

technical field [0001] The invention relates to the technical field of point cloud compression, in particular to a point cloud geometric lossy compression method based on voxel convolution. Background technique [0002] With the development of 3D scanning technology, 3D information is widely used in the real world, and the digitization of 3D information is becoming more and more important. Point cloud is an important way to express 3D scenes and 3D surfaces of objects. Point clouds are discretely distributed in three-dimensional space in the form of points, and each point contains information such as geometric position, color, and texture, allowing users to experience viewing from any angle. In practical applications, the point cloud is mainly obtained by sampling the scene or the surface of the object by the 3D scanning device. There are many points and a large amount of information, so the data volume of the point cloud is often very large. Considering the problem of stora...

Claims

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Application Information

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IPC IPC(8): H04N19/124H04N19/85G06T9/00
CPCG06T9/00H04N19/124H04N19/85
Inventor 胡强张迎梁
Owner PLEX VR DIGITAL TECH CO LTD
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