POINT CLOUDS Fast Course: Introduction to 3D Perception by Jeremy Cohen

POINT CLOUDS Fast Course: Introduction to 3D Perception

Learn the fundamentals of LiDAR detection and Point Cloud Processing for 3D Obstacle Detection.

What's included

✔️ Introduction to 3D Perception
✔️ The 3D Machine Learning guide
✔️ VIP Contact and support
✔️ One-on-one onboarding sessions (optional)

We live in a 3D world, and need 3D Machine Learning.

Imagine that you had cutting-edge 3D Machine Learning skills...

What do you know about Perception currently? A bit of Computer Vision? Some Machine Learning?

What if you took a leap forward and learned how to use 3D Point Cloud Data coming from advanced laser sensors to detect obstacles, analyse driveable areas, and track obstacles?

What if you added something relevant and powerful to your current skills?

Learning 3D Perception is tough; it doesn't have to be!

Every time you try to learn about LiDAR, it's the same struggle. You spend hours searching overviews, looking for videos, GitHub repos, and trying to understand how to deal with 3D data.

You might wonder "Where do I start?", "How do I start?", or "Is there Machine Learning for 3D data?"

The world of 3D perception and LiDAR (Light Detection And Ranging) is badly explained and clearly lacks an introduction.

Introducing "Point Clouds Fast Course: Introduction to 3D Perception" - The online course for beginners who rapidly want to increase their 3D Perception skills without spending hours browsing and figuring out how to do...

What you'll learn

✔️ How to process point cloud data in real-time
✔️ How to use Voxel Grid to make your project faster and lighter
✔️ What are the 5 Machine Learning techniques applied to 3D Data?
✔️ Driveable Area Detection in 3D using Consensus algorithms
✔️ How to detect obstacles and build a 3D bounding box around each one
✔️ The 2 techniques Engineers use to make obstacle detection powerful
☑️ Build your own obstacle detection project using real-world data

✔️ The 5 Steps to process point clouds data
✔️ What is a point cloud
✔️ All point cloud types  and file formats and how they work
✔️ How to find, read, and exploit a LiDAR Dataset
✔️ How LiDARs work
✔️ The Strength of time of flight systems and to exploit them
✔️ The Libraries engineers use and how they use them

Introduction to LiDAR & Point Clouds

Learn the fundamentals of 3D sensing. What are the different types of point clouds? Are there different ways to interpret the data?

LiDAR sensing, 3D Perception, Point Cloud Files, Point Cloud Formats, Overview of a detection process, Point Cloud Processing Libraries

A first approach to 3D Obstacle Detection

Computer Vision is about detecting obstacles in 2D. In this course, we'll do this in 3D using LiDAR sensors and Machine Learning techniques.

Techniques: Point Cloud Processing, Downsampling techniques, Segmentation, Unsupervised Learning techniques applied to 3D Data, Plane Fitting Algorithms, Clustering techniques, Optimization strategies, Bounding Box fitting in 3D

Fast Course, Fast Results

This course is fast. In under a few hours, you'll master point clouds and know how to create an algorithm for drivable area detection and 3D obstacle detection in real-time.

🧐Need more details?
Check the detailed summary.


What are the prerequisites to follow the course?

The course is in Python. Understanding Python is the only prerequisite to follow the course.

Do I need GPU Processing?

Everything will be done locally, but the files selected will allow you to process the point clouds without a GPU.

Can I implement the concepts taught in a real self-driving car?

The course is in Python. Translating this in C++ would be much more efficient for a real self-driving car. Other than that, all concepts taught are implemented today and used in autonomous driving.

Lead the new generation of 3D Machine Learning Engineers who will master a rising technology. It's a necessary skill facing a growing demand and a clear lack of introduction. Lead the way, be at the cutting-edge.

Enroll Now

Jeremy Cohen

Artificial Intelligence & Self-Driving Car Engineer, Head Dean of France School of AI, and Machine Learning Lecturer.
I started thinkautonomous.ai to help aspiring AI & Self-Driving Car Engineers to land their dream job. Working in the industry of the future requires skills and passion. You can build your skills here, where you'll create relevant projects that are used every day in autonomous robots & AI engines.

👉 Learn more