Plane fitting using 3D points

2019 3 26
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更新时间：2023 7 21
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I have my 3D points(lidar data) as a text file. can anyone suggest me a method for fitting the plane.

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Star Strider
Star Strider 2019-3-26

0 个投票

Try this:
P = bsxfun(@times, rand(49, 3), [1 10 100]); % Create Matrix [x, y, z]
B = [P(:,1), P(:,2), ones(size(P,1),1)] \ P(:,3); % Linear Regression
xv = [min(P(:,1)) max(P(:,1))];
yv = [min(P(:,2)) max(P(:,2))];
zv = [xv(:), yv(:), ones(2,1)] * B; % Calculate Regression Plane
figure
stem3(P(:,1), P(:,2), P(:,3), '.')
hold on
patch([min(xv) min(xv) max(xv) max(xv)], [min(yv) max(yv) max(yv) min(yv)], [min(zv) min(zv) max(zv) max(zv)], 'r', 'FaceAlpha',0.5)
hold off
grid on
xlabel('X')
ylabel('Y')
producing (with this set of random data):
Plane fitting using 3D points - 2019 03 26.png

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Hello star strider,
I already have 3D points and I'm need of a multiple planes for those points and not a single plane considering all the points. Can you suggest me any codes sir?
Star Strider
Star Strider 2019-3-26
I have no idea what you are asking.
If you have multiple sets of points ((Nx3) matrices), use my code for each set.
I'm doing research on 'Automated Building Extraction'. I have 3D point data for an urban region with no vegetation and ground points. Now the points I have is mostly on building roof. To extract each building roof separately, I need to fit separate planes on each building roof with the points that lie on the respective roofs. Since the points on each roof will be planar. I hope you understood my query. Can you help me to find a solution sir? Looking forward for your help sir.
Star Strider
Star Strider 2019-3-27
Just use my code with the roof coordinates instead of the random data.
It will be best to do each plane of each roof separately.
lidarFile = '/Users/Ashi/Downloads/las_data/Subset/LIDAR_subset.txt';
fp = fopen(lidarFile, 'r');
ALS = fscanf(fp, '%f %f %f', [3 inf]);
fclose(fp);
ALS = ALS';
x=ALS(:,1);
y=ALS(:,2);
z =ALS(:,3);
% Create Matrix [x, y, z]
B = [ALS(:,1), ALS(:,2), ones(size(ALS,1),1)] \ ALS(:,3); % Linear Regression
xv = [min(ALS(:,1)) max(ALS(:,1))];
yv = [min(ALS(:,2)) max(ALS(:,2))];
zv = [xv(:), yv(:), ones(2,1)] * B; % Calculate Regression Plane
figure
stem3(ALS(:,1), ALS(:,2), ALS(:,3), '.')
hold on
patch([min(xv) min(xv) max(xv) max(xv)], [min(yv) max(yv) max(yv) min(yv)], [min(zv) min(zv) max(zv) max(zv)], 'r', 'FaceAlpha',0.5)
hold off
grid on
xlabel('X')
ylabel('Y')
This is the code I'm using for my points. I have included my result image and the points I'm using and the original orthoimage.
VIGNESH BALAJI
VIGNESH BALAJI 2023-7-21
@Star Strider When I use your method, the plane fit for a 3D dataset is not a good fit, maybe because instead of regression you can calculate mean of the points and find a basis of the plane and fit the plane to the data.
When I used Plane Fit (Affine fit function to fit the data) https://nl.mathworks.com/matlabcentral/fileexchange/43305-plane-fit . I got a better fit

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