Deep Learning for 3D Object Detection

September 30, 2025 · View on GitHub

This repository serves as a tutorial for training and deploying a 3D object detector using MATLAB® R2024b-R2025b. It guides you through the processes of data processing, labeling, training, and deploying an object detection model. In this demonstration, simulated LiDAR data is collected from a vehicle navigating a parking lot. project overview

Video Tutorials

Learn how to use the code and apps described in this repository by following along with these videos:

Getting started

To get started, clone this repo to your local machine and open MATLAB or use this button to open the repository in MATLAB Online:

The repository follows the workflow shown below:

Project Overview

Understanding and Processing Point Clouds

This section covers data exporation and different ways one can preprocess LiDAR data. It gives an introduction to the Lidar Viewer App and explains the different exploration and processing option offered in the app. The file also contains scripts to do the same using MATLAB functions in a programmatic way.

Labeling Point Clouds

This section introduces the Lidar Labeler App and walks through the steps to interactively label objects of interest using the app. For this demo, the focus is on labeling cars, construction barrels and cones. The labeled data is then exported to be used for training a deep learning network.

Deep Learning

The third section goes into detail on training a point pillar network to detect the labeled objects in point clouds. This includes data augmentation, configuring the point pillar network, training and evaluating the results.

Code Generation

The final section demonstrates how the trained network can be deployed to a target system using MATLAB® Coder™. Two types of deployment are discussed:

Static library
ROS Nodes