Large-scale point cloud selection method for supporting lasso
A large-scale, lasso technology, applied in 3D modeling, image data processing, instruments, etc., can solve the problem of large amount of test calculation, and achieve the effect of improving the performance of calculation selection and reducing the amount of test calculation.
Active Publication Date: 2014-12-31
SHANGHAI LINCTEX DIGITAL TECH CO LTD
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[0006] The present invention provides a large-scale point cloud selection method that supports lasso, to solve the problem of large amount of calculation in the test of points in the polygon one by one, and eliminate most of the points in the polygon by sketching the lasso polygon Judgment, improve the efficiency of scanning point cloud area selection
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[0036] The technical solution of the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments, and the following embodiments do not constitute a limitation of the present invention.
[0037] a kind of like figure 1 The lasso-supported large-scale point cloud selection method shown includes steps:
[0038] Step 101, the user rotates the point cloud model to a suitable position to circle the target point cloud, and then sketches a lasso polygon to circle the target point cloud;
[0039] Step 102, constructing a rectangular covering for the delineated lasso polygon, and assigning internal, external, and boundary attributes to the rectangular units forming the rectangular covering respectively, and embedding all the line segments forming the lasso polygon into the rectangular unit;
[0040] Step 103, establishing an octree hierarchical structure of the input point cloud, and projecting the octree onto a plane perpen...
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The invention discloses a large-scale point cloud selection method for supporting lasso. The method comprises the following steps of: rotating a point could model by a user to an appropriate direction, then sketching a lasso polygon, and circling and selecting a target area; constructing a rectangular cover for the input lasso shape and respectively applying internal, external and boundary attributes, and embedding all line segments for consisting the lasso polygon into a rectangular unit; establishing an octree structure of the input point cloud model; and testing a point in the polygon by utilizing the inclusion relation of the matrix cover and octree node projection convex hulls. According to the invention, by utilizing the inclusion relation between projection octree node units and the polygon rectangular cover, the testing of most points in the polygon is rejected, thus scanning point cloud area selection efficiency can be improved.
Description
technical field [0001] The invention relates to the technical field of reverse engineering, in particular to a lasso-supported large-scale point cloud selection method. Background technique [0002] At present, the most common application mode in reverse engineering is to use scanning equipment based on optical principles to quickly measure the outer surface of parts or molds to form point cloud data, extract geometric features from them, and then reconstruct polygonal or NURBS surfaces. With the improvement of scanning hardware accuracy, the original scanning data can better approximate the surface shape of the physical model, and point cloud denoising filtering is usually not required. However, subtraction processing is always an indispensable key preprocessing step. First of all, according to the placement of the measured object, the scanned point cloud will inevitably contain non-target background data such as walls and supports; or the user only wants to retain a part ...
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IPC IPC(8): G06T17/00
Inventor 范然邱妮娜金小刚
Owner SHANGHAI LINCTEX DIGITAL TECH CO LTD