3D Data Science with Python

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3D Data Science Toolkit

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  • Complete Python code for all 18 chapters
  • Pre-processed sample datasets
  • Jupyter notebooks with step-by-step walkthroughs
  • Exclusive visualization examples not in the book
  • Advanced cheat sheets & bonus resources
3d data science with Python toolkit

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# Sample code from Chapter 9: 3D Shape Recognition
import numpy as np
import open3d as o3d

 

def extract_geometric_features(point_cloud, radius=0.1):
# Compute normals if not already present
if not point_cloud.has_normals():
  point_cloud.estimate_normals(
  search_param=o3d.geometry.KDTreeSearchParamHybrid(
  radius=radius, max_nn=30))

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What Readers Are Saying

"This book has completely transformed my approach to 3D data. The code examples are clear, practical, and directly applicable to real-world problems."
Sarah Johnson
Data Scientist at TechCorp
"As someone working with LiDAR data daily, the segmentation techniques covered in this book have saved me countless hours of manual processing."
Michael Chen
Geospatial Engineer
"The perfect balance of theory and practice. The companion materials made it easy to follow along and experiment with the concepts right away."
Elena Rodriguez
PhD Researcher, Computer Vision
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