3D Point Cloud Segmentation and Shape Recognition with Python

I share a hands-on Python approach to Automate 3D Shape Detection, Segmentation, Clustering, and Voxelization for Point Cloud Datasets. In this case, we study an example of an indoor dataset. By the end, you'll have a solid understanding of how to work with 3D point cloud datasets and perform advanced 3D shape recognition tasks using Python.

I go through a 7-step process that follows the different chapters.

🛠️ EPISODE RESOURCES (OPEN)
- Get the 🖥️Code, 🍇Data and 📘 Article: https://learngeodata.eu/3d-tutorials/

📚 OPEN TUTORIALS FOR 3D DATA PROCESSING
- Medium Articles:  Medium: florentpoux

🎓 ONLINE COURSES FOR 3D DATA SCIENCE
- 3D Geodata Academy: https://learngeodata.eu

🙋 FOLLOW ME
- Linkedin:  LinkedIn: florent-poux-point-cloud

📄 Chapters
[00:00:00]: Introduction to 3D point cloud segmentation
[00:01:45]: Step 1. 3D Python Environment Setup
[00:02:04]: Step 2. 3D Data Preparation
[00:02:58]: Step 3. 3D Point Cloud Pre-Processing
[00:08:43]: Step 4. Parameter Setting
[00:09:34]: Step 5. RANSAC for 3D Plane Segmentation
[00:12:10]: Step 6. Multi-Order RANSAC
[00:14:46]: Step 7. 3D Euclidean Refinement
[00:16:58]: Conclusion and Perspectives

• #science_technology
• #point_cloud
• #3d_python
• #segmentation
• #clustering
• #3d_data_science
• #3d_shape_recognition