Fitting a plane through a 3D point data

Question

ha ha 2018-5-6

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/399291-fitting-a-plane-through-a-3d-point-data

编辑： Matt J 2018-5-6

1.txt

For example, i have 3d point cloud data [xi, yi, zi] as the attachment .txt file. I want to fit a plane to a set of 3D point data. What kind of method to do that?

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Matt J 2018-5-6

How does one know that M and L are different planes and not just noise? Is there a known upper bound on the noise? A known lower bound on the separation distance between M and L?

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Answer 1

Matt J 2018-5-6

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https://ww2.mathworks.cn/matlabcentral/answers/399291-fitting-a-plane-through-a-3d-point-data#answer_318920

You will probably have to implement a RANSAC plane fitting routine.

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ha ha 2018-5-6

Thanks.

Matt J 2018-5-6

编辑：Matt J 2018-5-6

One approach you might consider is to take planar cross sections of your data. This will give 2D data for a line, with outliers. Then you can apply a ready-made RANSAC line-fitter, like the one I linked you to. From line fits in two or more cross-secting planes you should be able to construct the desired plane K.

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Answer 2

Walter Roberson 2018-5-6

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https://ww2.mathworks.cn/matlabcentral/answers/399291-fitting-a-plane-through-a-3d-point-data#answer_318888

在 MATLAB Online 中打开

data = load('1.txt');
coeffs = [data(:,1:2), ones(size(data,1),1)]\data(:,3);

The equation of the plane is then coeffs(1)*x + coeffs(2)*y - coeffs(3) = z

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ha ha 2018-5-6

编辑：ha ha 2018-5-6

From your answer, I plot the surface as below image. But That plane is not same as my expected plane. If we use the formulas as your proposed method, the plane is fitting through all points & will be slightly different with my expected plane K(=plane M)

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Answer 3

Matt J 2018-5-6

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https://ww2.mathworks.cn/matlabcentral/answers/399291-fitting-a-plane-through-a-3d-point-data#answer_318916

编辑：Matt J 2018-5-6

在 MATLAB Online 中打开

   xyz=load('1.txt');
    xyz(xyz(:,2)>40, :)=[];
   mu=mean(xyz,1);
   [~,~,V]=svd(xyz-mu,0);
   normal=V(:,end).';
   d=normal*mu';

The equation of the plane is then xyz*normal.' = d

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ha ha 2018-5-6

In my question: Plane M contains a large number of point data when compared with plane L(i.e., 90%). I wanna find the plane can cover large number points as plane M. Example: in the general, there are some outlier(or noise) points. So, the result will be affected significantly. Because you are using "least square regression method" as I guessed

Matt J 2018-5-6

How does one know that M and L are different planes and not just noise? Is there a known upper bound on the noise? A known lower bound on the separation distance between M and L?

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