def detect_outliers(points, threshold=3): mean = np.mean(points, axis=0) std_dev = np.std(points, axis=0) distances = np.linalg.norm(points - mean, axis=1) outliers = distances > (mean + threshold * std_dev) return outliers
Automatic Outlier Detection and Removal
Here's a feature idea:
def remove_outliers(points, outliers): return points[~outliers]
# Detect and remove outliers outliers = detect_outliers(mesh.vertices) cleaned_vertices = remove_outliers(mesh.vertices, outliers) Meshcam Registration Code
The Meshcam Registration Code! That's a fascinating topic.
import numpy as np from open3d import *
# Register mesh using cleaned vertices registered_mesh = mesh_registration(mesh, cleaned_vertices) This is a simplified example to illustrate the concept. You can refine and optimize the algorithm to suit your specific use case and requirements.
Implement an automatic outlier detection and removal algorithm to improve the robustness of the mesh registration process. def detect_outliers(points, threshold=3): mean = np
To provide a useful feature, I'll assume you're referring to a software or tool used for registering or aligning 3D meshes, possibly in computer vision, robotics, or 3D scanning applications.