Deep point cloud compression coding method based on full self-attention network

A technology of compression coding and attention, which is applied in the direction of instruments, character and pattern recognition, electrical components, etc., can solve the problems of not making full use of the correlation of each point, not fully considering the sparsity of point clouds, and being difficult to apply in practice. Effectiveness and practicability, high coding efficiency, low complexity, and the effect of reducing redundant calculations

Pending Publication Date: 2022-04-15
SUN YAT SEN UNIV
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Problems solved by technology

One is that the method based on voxel input does not fully consider the sparsity of the point cloud, and the complexity is too high to be practically

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  • Deep point cloud compression coding method based on full self-attention network
  • Deep point cloud compression coding method based on full self-attention network
  • Deep point cloud compression coding method based on full self-attention network

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Embodiment Construction

[0048] 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, not to limit the present application.

[0049] figure 1 Shown is the flow chart of the deep point cloud compression coding method based on the full self-attention network provided by the embodiment of the present invention, the method includes:

[0050] Construct a point cloud full self-attention network, which includes an encoder and a decoder;

[0051] Obtain the training data, construct the chamfering distance objective function to train the point cloud full self-attention network;

[0052] Input the point cloud data to the trained point cloud full self-attention network, use the encoder to perform feature sampling processi...

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Abstract

The invention discloses a depth point cloud compression coding method based on a full-self-attention network, and the method comprises the steps: constructing a point cloud full-self-attention network which comprises an encoder and a decoder; obtaining training data, and constructing a chamfering distance objective function to train the point cloud full-self-attention network; inputting point cloud data into the trained point cloud full-self-attention network, performing feature sampling processing on the point cloud data by using an encoder to obtain a point cloud code, and completing point cloud compression; and reconstructing point cloud data by using a decoder according to the point cloud code to complete point cloud decompression. According to the method, learning of local and global correlation among points of the point cloud is enhanced based on network training of a chamfering distance objective function, point cloud codes capable of accurately representing semantic information of the point cloud are obtained through feature sampling of the point cloud by a coding machine, safety and stability of storage and transportation of the point cloud information are guaranteed, and the method is suitable for large-scale popularization and application. The method can be widely applied to the technical field of point cloud compression coding.

Description

technical field [0001] The invention relates to the technical field of point cloud compression encoding, in particular to a deep point cloud compression encoding method based on a full self-attention network. Background technique [0002] Learning-based point cloud compression is a specific implementation method of point cloud compression. A point cloud is a collection of discrete points with 3D geometric locations and other attributes (e.g., color, opacity, etc.), which can be used to efficiently represent stereoscopic data such as 3D scenes and objects. Realizing efficient point cloud compression methods can promote the progress of various point cloud applications, such as augmented reality, autonomous driving, 3D free-viewpoint video, holographic transmission, etc. Recently, learning-based methods for point cloud compression have emerged, using powerful deep learning networks to learn the parameters of point cloud compression encoders. There are two main problems in exi...

Claims

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Application Information

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IPC IPC(8): H04N19/597H04N13/00G06K9/62G06V10/774
Inventor 梁凡梁祖杰郁鹏鹏
Owner SUN YAT SEN UNIV
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