Uploaded October 2025 | Updated September 2026, 2 weeks ago
Dive into deep learning to train a 3D object detector using labeled lidar data. Learn how to organize point cloud and label data for deep learning and how to augment your data to create a more robust detector. Then, explore the different options and information needed when creating and training a neural network, using a PointPillars network as an example. Finally, see how to use your trained 3D object detector and save it for later use or sharing.
Check out the other videos in the Deep Learning for 3D Object Detection series: youtube.com/playlist?list=PLn8PRpmsu08r-T6JFLhf-Bp3QcEpzE1ex
Related Resources:
- Access material for this video: bit.ly/3D-object-detection
- Data Augmentations for Lidar Object Detection Using Deep Learning: bit.ly/4nOe0eI
- trainingOptions - Options for Training Deep Learning Neural Network: bit.ly/47uyZv8
Chapters:
0:00 Introduction
0:23 Importing labeled data
0:45 What is a datastore?
1:36 Formatting labeled point clouds for deep learning
2:10 Augmenting a point cloud dataset
5:36 Creating a PointPillars object detector
7:45 Training a neural network
9:30 Using the trained 3D object detector
10:12 Improving model performance
11:01 Saving the 3D object detector
11:19 Conclusion
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Learn more about MATLAB: goo.gl/8QV7ZZ
Learn more about Simulink: goo.gl/nqnbLe
See what's new in MATLAB and Simulink: goo.gl/pgGtod
© 2025 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc.
See mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Dive into deep learning to train a 3D object detector using labeled lidar data. Learn how to organize point cloud and label data for deep learning and how to augment your data to create a more robust detector. Then, explore the different options and information needed when creating and training a neural network, using a PointPillars network as an example. Finally, see how to use your trained 3D object detector and save it for later use or sharing.
Check out the other videos in the Deep Learning for 3D Object Detection series: youtube.com/playlist?list=PLn8PRpmsu08r-T6JFLhf-Bp3QcEpzE1ex
Related Resources:
- Access material for this video: bit.ly/3D-object-detection
- Data Augmentations for Lidar Object Detection Using Deep Learning: bit.ly/4nOe0eI
- trainingOptions - Options for Training Deep Learning Neural Network: bit.ly/47uyZv8
Chapters:
0:00 Introduction
0:23 Importing labeled data
0:45 What is a datastore?
1:36 Formatting labeled point clouds for deep learning
2:10 Augmenting a point cloud dataset
5:36 Creating a PointPillars object detector
7:45 Training a neural network
9:30 Using the trained 3D object detector
10:12 Improving model performance
11:01 Saving the 3D object detector
11:19 Conclusion
--------------------------------------------------------------------------------------------------------
Get a free product trial: goo.gl/ZHFb5u
Learn more about MATLAB: goo.gl/8QV7ZZ
Learn more about Simulink: goo.gl/nqnbLe
See what's new in MATLAB and Simulink: goo.gl/pgGtod
© 2025 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc.
See mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
