The 3D Data Science Hub Resources
Welcome to the 3D Data Science Hub 🐦🔥
Four years ago, I spent countless late nights piecing together information from dozens of scattered sources, just trying to figure out how to properly filter a point cloud. Today, I’m building that resource I wish existed back then.
This hub contains everything I’ve learned working hands-on with 3D data across industries – from mapping underground mines to helping robots identify objects. No academic jargon, no unnecessary theory – just practical techniques that actually work in the real world.
Here’s how to get the most out of this resource:
- Latest Releases: I add new tutorials weekly based on reader questions and problems I’ve recently solved in my own projects. Start here for cutting-edge techniques.
- 3D Reconstruction: If you’re looking to create 3D models from images or sensor data, this section walks you through the entire pipeline from capture to optimization.
- 3D Processing and Algorithms: The foundation of all 3D work. Learn exactly how to clean, filter, register and segment your data with code that’s optimized to handle massive datasets.
- 3D Machine Learning and AI: Ready to add intelligence to your 3D pipeline? These tutorials break down complex deep learning approaches into implementable steps with real-world examples.
Each tutorial includes complete code samples, parameter recommendations, and common pitfalls to avoid – all based on solutions I’ve personally implemented and tested.
Have a specific challenge? Drop me a note, and I might feature the solution in next week’s release.
🎼 Note: This tutorial is offered to you as part of my goal to open 99% of my work. To support this vision while joining the top 1% of 3D Experts, you can pick a course program to specialize in 3D Reconstruction, 3D Data Processing, or 3D Deep Learning.
Have a great read 📜
Latest Releases
I just pushed new tutorials every month based on questions you’ve been asking me. Each tutorial may include the full code and parameter settings you can adapt to your specific requirements.
- 3D Reconstruction Pipeline: Photo to Model Workflow Guide
- Synthetic Point Cloud Generation of Rooms: Complete 3D Python Tutorial
- 3D Generative AI: 11 Tools (Cloud) for 3D Model Generation
- 3D Gaussian Splatting: Hands-on Course for Beginners
- Building a 3D Object Recognition Algorithm: A Step-by-Step Guide
- 3D Sensors Guide: Active vs. Passive Capture
- 3D Mesh from Point Cloud: Python with Marching Cubes Tutorial
- How To Generate GIFs from 3D Data with Python
- 3D Reconstruction from a Single Image: Tutorial
Towards 3D Reconstruction
I remember my first attempt at 3D reconstruction – hours of capturing images only to end up with a messy, half-formed model.
After years of trial and error, I’ve distilled what actually works. These tutorials skip the theory overload and show you exactly how to capture real-world objects and transform them into clean digital models. Whether you’re starting from scratch or trying to improve your existing workflow, you’ll learn the practical techniques I use daily to create professional-quality 3D assets from ordinary photos.
3D Reconstruction Tutorials
- 3D Reconstruction Pipeline: Photo to Model Workflow GuideThe 3D Reconstruction pipeline, to get from 2D photographs to 3D models follows a structured path. This path consists of distinct steps that build upon each other to transform flat images into spatial information. Understanding this pipeline is crucial for anyone looking to create high-quality 3D reconstructions. Let me explain…… Read more: 3D Reconstruction Pipeline: Photo to Model Workflow Guide
- 3D Gaussian Splatting: Hands-on Course for BeginnersThis Tutorial targets 3D Reconstruction and 3D Real-Time Visualization leveraging 3D Gaussian Splatting. In the end, you will be able to generate 3D Point Clouds and 3D Renderings from a Set of Images and/or Videos. 3D Reconstruction with 3D Gaussian Splatting In the rapidly evolving landscape of 3D modeling, innovations… Read more: 3D Gaussian Splatting: Hands-on Course for Beginners
- 3D Sensors Guide: Active vs. Passive CaptureAlright, let’s cut to the chase: 3D sensors are transforming industries. It’s not just buzz – it’s about self-driving cars that see, not guess, and VR that’s actually immersive. This guide dives into the tech, focusing on what matters: how the sensors work and which ones are right for you.… Read more: 3D Sensors Guide: Active vs. Passive Capture
- 3D Reconstruction from a Single Image: TutorialThis tutorial targets Monocular Depth Estimation for 3D Reconstruction (Point Cloud, 3D Mesh). In the end, you will be able to generate 3D Models from any photo or AI-generated image. 3D Reconstruction with 3D Deep Learning 3D Reconstruction Combining the power of deep learning unlocks the ability to get 3D… Read more: 3D Reconstruction from a Single Image: Tutorial
- 2D Image to 3D ModelThis Live Course / Workshop series will give you a linear, hands-on solution for turning a monocular 2D Image to 3D Model. This means that you will be able to take any AI-generated image, and make it a 3D Model. 3D Reconstruction: 2D Image to 3D Model Pulling objects straight… Read more: 2D Image to 3D Model
Get the Complete Operating System Course
3D Reconstructor OS is your perfect companion, transforming the complex theory of multi-view geometry into practical skills for building high-quality 3D models from images and sensors.
3D Processing and Algorithms
Let’s be honest – processing raw 3D data is where most projects fall apart. I’ve watched brilliant engineers get stuck trying to handle noisy point clouds or optimize complex meshes. My approach? Break it down into simple, repeatable steps.
These tutorials focus on the 20% of algorithms that solve 80% of real-world problems. You’ll implement Python code that actually runs on large datasets and learn the exact parameters I use to filter, register, and segment 3D data efficiently.
3D Data Processing and Algorithms Tutorials
- Synthetic Point Cloud Generation of Rooms: Complete 3D Python TutorialThis tutorial teaches you how to develop a Synthetic point cloud generation engine in Python using NumPy and Open3D, enabling the creation of unlimited labeled 3D data for machine learning. By automating the process, you can save time, reduce costs, and generate realistic training datasets without expensive equipment or manual labeling.
- 3D Mesh from Point Cloud: Python with Marching Cubes TutorialThis tutorial dives deep into the Marching Cubes algorithm, a powerful technique for meshing 3D point clouds using Python. We transform a point cloud into a 3D mesh, experiment with various parameters, and build a simple web app with a graphical user interface (GUI). This method bypasses the limitations of… Read more: 3D Mesh from Point Cloud: Python with Marching Cubes Tutorial
- How To Generate GIFs from 3D Data with PythonComplete Tutorial to Automate 3D Data Visualization. Use Python to convert point clouds and 3D models into GIFs & MP4s for easy sharing and collaboration. As a data scientist, you know that effectively communicating your insights is as important as the insights themselves. But how do you communicate over 3D… Read more: How To Generate GIFs from 3D Data with Python
- 3D Reconstruction Methods, Hardware and Tools for 3D Data ProcessingThis article provides the best recommendations and directions for every dataset, tool, software, hardware, code setup, and library for 3D Reconstruction Methods. This is the first Chapter of the 3D Master Toolbox, which provides 47 methods and AI tools for 3D. This article shares online solutions for 3D Data Curation… Read more: 3D Reconstruction Methods, Hardware and Tools for 3D Data Processing
- A Quick Dive into Modern Point Cloud WorkflowDesigning a point cloud workflow is a powerful first-hand approach in 3D data projects. This article explores how processing massive point clouds efficiently isn’t about having more computing power. It’s about being more innovative with the resources you have. After analyzing hundreds of real-world projects, let me share identified patterns… Read more: A Quick Dive into Modern Point Cloud Workflow
Get the Complete Operating System Course
3D Segmentor OS directly aligns with these challenges, teaching you production-tested techniques to partition, filter and analyze 3D data with unprecedented efficiency.
3D Machine Learning and AI
When I first applied deep learning to 3D data, I wasted months on architectures that looked impressive in research papers but failed on my projects. Through countless experiments, I’ve identified what truly works in production. These tutorials cut through the hype and show you how to build and train models that reliably understand 3D space. I’ll share the exact implementation details that transformed my work from interesting experiments to reliable solutions that deliver consistent results.
- 3D Generative AI: 11 Tools (Cloud) for 3D Model GenerationThis article compares the top 11 cloud tools that leverage 3D Generative AI. These 3D solutions simplify workflows and open new avenues for creative expression. These tools include Meshy, 3D AI Studio, Masterpiece X, Alpha 3D, Sloid, 3DFY AI, Spline, Luma AI, Rococo Vision, Ponzu, and Deep Motion. Each tool… Read more: 3D Generative AI: 11 Tools (Cloud) for 3D Model Generation
- Building a 3D Object Recognition Algorithm: A Step-by-Step GuideThis learning piece provides a step-by-step guide for developing your 3D Object Recognition application, from data collection to deployment. Learn about the different approaches to data collection and preparation, the importance of feature engineering, and the process of selecting and training a machine learning model. Discover best practices for evaluating… Read more: Building a 3D Object Recognition Algorithm: A Step-by-Step Guide
- 3D Deep Learning Essentials: Ressources, Roadmaps and SystemsDive into the fascinating world of 3D with deep learning, where objects come alive and possibilities are beyond 2D pixels. Few technologies can change the landscape of Computer Science at the pace and impact that Artificial Intelligence does. But one specific aspect is even more profound: 3D Deep Learning. Why Learn… Read more: 3D Deep Learning Essentials: Ressources, Roadmaps and Systems
- Tutorial for 3D Semantic Segmentation with Superpoint TransformerWe dive into SuperPoint Transformer, a novel approach for 3D semantic segmentation presented in the research paper “Efficient 3D Semantic Segmentation with Superpoint Transformer“. We also explore the core concepts, examine the research methodology, and unpack the key takeaways from the paper, with one of its author. 1. Introduction to… Read more: Tutorial for 3D Semantic Segmentation with Superpoint Transformer
Get the Complete Operating System Course
3D Intelligence OS bridges the gap between theoretical AI concepts and practical 3D applications, showing exactly how to implement and train neural networks that understand spatial data.
3D Data Science Hub: FAQ
I built this hub as a central resource for practical 3D data science knowledge. It’s the collection of techniques, code samples, and solutions I’ve developed over years of working with 3D data across industries. No fluff, just what works.
Absolutely. While I don’t water down the technical content, each tutorial starts with fundamentals before diving deeper. I’ve structured everything assuming you know basic Python but might be new to 3D concepts.
I release new tutorials weekly, focusing on either reader questions or solutions to problems I’ve recently tackled in my professional work.
Most tutorials leverage Open3D, NumPy, and scikit-learn as the foundation. For visualization, I use PyVista, and for deep learning components, PyTorch with specialized 3D extensions. I always include full environment setup instructions.
For basic processing and algorithms, any decent laptop will work. For the deep learning tutorials, having access to a GPU will significantly speed things up, especially when working with larger datasets. I note hardware requirements at the beginning of each tutorial.
Yes! All code is provided under MIT license. Use it however you want in your projects. I only ask that if you find it valuable, share the hub with others who might benefit.
The best way is to post your specific question in the community forum (linked below each tutorial). I check it regularly, and often other practitioners with similar experiences will jump in with solutions.
The principles apply broadly, but parameters often need adjustment for different data types. In the “Customization” section of each tutorial, I explain which parameters to tune first and how different data characteristics affect them.
The hub expands on and complements the book’s content. While the book provides a structured curriculum from fundamentals to advanced topics, the hub offers more specialized techniques, cutting-edge approaches, and ongoing updates as the field evolves.
Not at all. Each tutorial stands on its own. That said, the book provides a more systematic foundation that makes the hub’s more advanced content easier to absorb.
3D Data Science Hub: Complimentary Resources
At the 3D Data Science Hub, I believe in standing on the shoulders of giants. These aren’t just random links I’ve collected—they’re the exact resources I turn to daily in my own work with 3D data. Each one has saved me countless hours of troubleshooting and experimentation. Whether you’re looking for comprehensive documentation on processing algorithms, cutting-edge research implementations, or high-quality datasets to test your methods, this curated collection represents the most valuable tools in the 3D data science ecosystem. I’ve personally vetted each resource for practical utility and included why it might be relevant to your specific projects. Bookmark these links—they’ll become indispensable as you apply the techniques from our tutorials to your own unique challenges.
| Resource | Description | Best For | Link |
|---|---|---|---|
| Open3D Documentation | Comprehensive library for 3D data processing with detailed API references and tutorials | Processing point clouds, meshes, and RGBD images | open3d.org |
| Point Cloud Library (PCL) | Extensive C++ library with Python bindings for point cloud processing | Industrial-grade point cloud operations | pointclouds.org |
| PyTorch3D | Deep learning library for 3D data with differentiable renderers | 3D deep learning research and applications | pytorch3d.org |
| COLMAP | Structure-from-Motion and Multi-View Stereo pipeline | Photogrammetric 3D reconstruction | colmap.github.io |
| OpenTopography | Repository of high-resolution topographic data and tools | Geospatial and terrain analysis | opentopography.org |
| Papers With Code (3D Vision) | Collection of state-of-the-art research papers with implementation code | Staying current with cutting-edge techniques | paperswithcode.com/task/3d-vision |
| ModelNet | Princeton’s benchmark dataset for 3D shape classification and retrieval | Training and benchmarking 3D models | modelnet.cs.princeton.edu |
| Stanford 3D Scanning Repository | Collection of high-quality 3D scans for research | Testing reconstruction and processing algorithms | graphics.stanford.edu/data/3Dscanrep |
| NeRF-Studio | Framework for Neural Radiance Fields research and applications | Novel view synthesis and neural rendering | nerf.studio |
| 3D Machine Learning Resources by Yuxuan | Curated list of papers, datasets, and code for 3D machine learning | Literature review and research guidance | github.com/timzhang642/3D-Machine-Learning |
Next Steps 👣
Open-Access Tutorials
You can continue learning standalone 3D skills through the library of 3D Tutorials
Latest Tutorials
- 3D Reconstruction Pipeline: Photo to Model Workflow Guide
- Synthetic Point Cloud Generation of Rooms: Complete 3D Python Tutorial
- 3D Generative AI: 11 Tools (Cloud) for 3D Model Generation
- 3D Gaussian Splatting: Hands-on Course for Beginners
- Building a 3D Object Recognition Algorithm: A Step-by-Step Guide
Instant Access to Entire Learning Program (Recommended)
If you want to make sure you have everything, right away, this is my recommendation:
1. The 3D Master Bundle: 3D Reconstruction, Processing and AI Systems (Lifetime)
Cherry Pick a single aspect
If you want to make sure you have everything, right away, this is my recommendation (ordered):
1. Get the fundamental 3D training (3D Bootcamp)
1. 3D Reconstruction (Creator OS)