I would like to suggest a feature in RevoScan software that could significantly simplify working with 3D scans. It involves the ability to align the point cloud to the coordinate system by approximating a surface through a selection of points in the point cloud.
Feature Description:
The idea is that users can select a group of points in their point cloud (see attached screenshot). After selecting these points, there should be an option to choose a plane (X, Y, or Z plane) to which this surface should be aligned.
Process:
- Selection of points in the point cloud (screenshot).
- Choosing the plane (X, Y, Z) to which the surface should be aligned.
- The software uses the method of approximation to draw a surface through the selected points and aligns this surface to the chosen coordinate plane.
Benefits:
Improved editing of the point cloud: Aligning to a plane allows users to more effectively edit and modify the point cloud.
Creation of a better mesh: An aligned point cloud leads to a higher quality and more precise mesh.
Easier model integration: Aligned models can be more easily integrated and edited in other software.
Increased accuracy and efficiency: This feature could enhance the precision of 3D models and make the editing process more efficient.
I believe this feature would provide significant added value to RevoScan and its users. It would improve flexibility and precision in editing 3D scans and significantly enhance the end product.
I look forward to feedback and hope that this idea could be considered in future updates.
Python example for alignment with code, after that u can snap this plane to your coordinate system XY or Z:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# generate random 3d points for testing
np.random.seed(0)
n_points = 100
x = np.random.uniform(-5, 5, n_points)
y = np.random.uniform(-5, 5, n_points)
z = 3*x + 2*y + np.random.normal(0, 2, n_points) # random plain with noise
# calculate plain with smallest quads
A = np.column_stack((x, y, np.ones(n_points)))
C, _, _, _ = np.linalg.lstsq(A, z, rcond=None)
# create plain
xx, yy = np.meshgrid(np.linspace(-5, 5, 10), np.linspace(-5, 5, 10))
zz = C[0]*xx + C[1]*yy + C[2]
# visualisation for watch
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(x, y, z, color='b', label='random points')
ax.plot_surface(xx, yy, zz, color='r', alpha=0.5, rstride=100, cstride=100)
ax.set_xlabel('X-Axis')
ax.set_ylabel('Y-Axis')
ax.set_zlabel('Z-Axis')
ax.legend()
plt.show()
