Description

Introduction
This training focuses on building practical expertise in 3D object detection using Python. Participants learn to process point cloud data from LiDAR and other 3D sensors, implement neural network architectures for detection, and deploy end-to-end 3D detection pipelines. The course emphasizes hands-on experience with Python libraries, neural network design, and real-world applications in autonomous driving, robotics, and AR/VR.

Prerequisites
Basic Python programming
Familiarity with machine learning and deep learning concepts
Knowledge of NumPy, PyTorch or TensorFlow
Optional: Experience with 2D computer vision

Table of Contents
1. Introduction to 3D Object Detection
 1.1 2D vs 3D vision
 1.2 Applications: Autonomous vehicles, robotics, AR/VR
 1.3 Overview of point clouds, meshes, and voxel representations

2. Python for 3D Data Processing
 2.1 Libraries: Open3D, PyTorch3D, NumPy
 2.2 Visualizing and handling point clouds
 2.3 Preprocessing: filtering, downsampling, segmentation

3. Neural Network Architectures for 3D Detection
 3.1 PointNet and PointNet++
 3.2 VoxelNet and 3D CNNs
 3.3 Transformer-based 3D detectors

4. Data Preparation and Augmentation
 4.1 LiDAR and depth sensor datasets
 4.2 Augmentation techniques for 3D data
 4.3 Training, validation, and test splits

5. Implementing 3D Detection Pipelines in Python
 5.1 Point cloud classification and segmentation
 5.2 3D bounding box detection
 5.3 Multi-modal sensor fusion (LiDAR + Camera)

6. Evaluation Metrics and Benchmarking
 6.1 IoU, mAP, and 3D detection metrics
 6.2 Standard datasets: KITTI, nuScenes
 6.3 Performance optimization techniques

7. Real-World Applications and Deployment
 7.1 Robotics and autonomous systems
 7.2 AR/VR environments
 7.3 Edge deployment and real-time inference

8. Hands-On Projects
 8.1 Building a PointNet-based object detector
 8.2 VoxelNet pipeline implementation
 8.3 Real-time detection with fused sensor data

Participants gain practical expertise in Python-based 3D object detection, mastering point cloud processing, neural network architectures, and deployment strategies. By the end, learners are equipped to implement robust 3D detection systems for diverse real-world applications.