Three-dimensional point cloud affine registration method and system based on pseudo Huber loss function
A technology of 3D point cloud and loss function, which is applied in image data processing, instrumentation, 3D modeling, etc. It can solve problems such as wrong registration results, affect the accuracy of point cloud registration, and poor registration results, so as to improve the accuracy Effect
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Embodiment 1
[0046] The 3D point cloud registration technology is to match the collected point cloud data, find a series of spatial transformation relations of two sets of point cloud datasets, and through these spatial transformations, make the corresponding points of the two point cloud datasets in the spatial position As consistent as possible, it is mainly divided into rigid registration and non-rigid registration. Among them, the simplest non-rigid registration is affine registration.
[0047] The purpose of 3D point cloud affine registration is to establish the spatial correspondence between two point clouds and find the optimal affine transformation between them. Usually, 3D point cloud affine registration includes solving two contents: one is to establish the correspondence between two point clouds; the other is to solve the affine transformation between two point clouds.
[0048] given shape point cloud and model point cloud where N p and N q Both are positive integers, assu...
Embodiment 2
[0111] On the basis of the first embodiment above, this embodiment provides a 3D point cloud affine registration system based on pseudo-Huber loss function, please refer to image 3 , image 3 It is a schematic structural diagram of another 3D point cloud affine registration system based on a pseudo-Huber loss function provided by an embodiment of the present invention. The system includes:
[0112] Data acquisition module 1, used to acquire shape point cloud and model point cloud
[0113] The model building module 2 is used to establish a three-dimensional point cloud affine registration model based on a pseudo-Huber loss function according to the shape point cloud and the model point cloud; the built model is expressed as:
[0114]
[0115] Among them, A represents the affine matrix, t represents the translation vector, c represents the spatial correspondence between the shape point cloud and the model point cloud, b represents the outlier threshold, and p i Represe...
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