Point cloud processing method based on distributed parameter coding
A processing method and distribution parameter technology, applied in image data processing, neural learning methods, instruments, etc., can solve problems such as large amount of calculation, inability to guarantee Lipschitz continuity of point cloud data, poor interpretation of parameters, etc., to reduce data volume effect
Active Publication Date: 2020-10-13
ZHEJIANG UNIV
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[0009] Using distribution parameters to represent point cloud data can skip the step of discretization and obtain continuous data, while maintaining the global information and three-dimensional information of point cloud data including special points; however, it also has some disadvantages : First of all, it has a large amount of calculation, and it needs to train the neural network according to the current point cloud data; secondly, it cannot guarantee the Lipschitz continuity of the point cloud data in the parameter space; finally, the parameter interpretation used to represent the point cloud distribution Poor sex
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[0028] In order to describe the present invention more specifically, the technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.
[0029] The point cloud processing method based on the distribution parameter coding of the present invention can represent the dynamic distribution of point cloud data in real time, and can also compress the existing point cloud data. The method first imports the position data Data[x, y of the point cloud at the current moment , z], assuming that the random point cloud distribution of the environment at the current moment is Pr([x, y, z]), a certain amount of random point cloud data of the environment is randomly generated according to this probability distribution function, and the previously imported point cloud data is labeled 1. Label the point cloud data randomly generated from the environment as 0; then train a neural network with a given structure a...
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The invention discloses a point cloud processing method based on distributed parameter coding, and the method comprises the steps: carrying out the fitting of the distribution state of point cloud through a training neural network, representing a probability density function of the current point cloud distribution, taking a parameter at the moment as a feature for representing the current point cloud data, and successfully compressing a large number of point cloud data to the magnitude of a parameter space. Meanwhile, aiming at the condition of continuously obtaining the point cloud, the pointcloud parameters can be used as the output of the circulation unit in the neural network, so the change trend of the point cloud data distribution along with the change of the external environment ispredicted.
Description
technical field [0001] The invention belongs to the technical field of point cloud data compression processing, and in particular relates to a point cloud processing method based on distribution parameter coding. Background technique [0002] With the rapid development of 3D data acquisition equipment, it is becoming more and more convenient and fast to use 3D point cloud to describe 3D objects and reproduce 3D scenes. 3D point cloud is a new data format for recording and representing the surface information of three-dimensional objects; point cloud data is a collection of a series of points in space, which contain three-dimensional coordinates and one or more attribute information, such as color, Normal vector, reflection strength, etc. Compared with other 3D data formats, point cloud has the advantages of convenient acquisition and simple processing, so it is widely used in various emerging fields, such as augmented reality (AR), autonomous driving, and 3D printing. [0...
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IPC IPC(8): G06T17/00G06N3/04G06N3/08
CPCG06T17/00G06N3/08G06N3/045
Inventor 吕攀钱广一李红杨国青吴朝晖
Owner ZHEJIANG UNIV