Master 3D AI Vision with Deep Learning – From Fundamentals to Advanced Architectures
Build expert-level skills in 3D deep neural networks, and real-world AI applications. Learn 3D CNNs, PointNet++, semantic segmentation, 3D object detection, and beyong
Implement and Optimize models with Python, Pandas3D, OpenCV, Keras, PyTorch, PyTorch3D
In-depth knowledge with Quizzes, Assignments and Realtime Projects
This comprehensive course delves into the exciting and rapidly growing field of 3D Deep Learning, empowering learners with the theoretical foundation and hands-on expertise to develop state-of-the-art AI-powered computer vision systems. The course bridges the gap between traditional 3D computer vision techniques and modern deep learning approaches that operate directly on 3D data types like point clouds, meshes, and volumetric data.
Starting with an Introduction to 3D Deep Learning, you’ll explore 3D data structures, how they differ from 2D, and their critical role in robotics, autonomous vehicles, medical imaging, and AR/VR. The curriculum covers essential representations such as voxels, point clouds, and meshes, along with preprocessing steps like normalization, noise reduction, alignment, and augmentation.
You’ll gain a comparative understanding of traditional vs. deep learning-based 3D computer vision techniques, supported by practical case studies. As the course progresses, you’ll dive deep into:
You’ll also learn data augmentation techniques tailored for 3D data (rotation, scaling, synthetic data), ensuring robustness and generalization. In the advanced modules, we introduce 3D Generative Models (GANs, VAEs) to explore realistic 3D shape and scene generation.
The course concludes with Transfer Learning in 3D Deep Learning, showcasing how to leverage pretrained models for tasks with limited labeled data.
Whether you’re an AI practitioner, vision engineer, or researcher, this course equips you with the skills, frameworks, and tools to design, build, and deploy intelligent 3D vision systems. You’ll graduate with the confidence to implement and evaluate 3D deep learning pipelines in real-world scenarios.
This advanced course is divided into 12 carefully crafted modules to help you master each area of 3D Computer Vision with Generative AI. Each module includes lectures, hands-on notebooks, datasets, quizzes, and assignments.
Environment setup, tools, libraries, datasets, course walkthrough.
Point clouds, voxel grids, meshes, depth maps, RGB-D, and volumetric data.
Cleaning, sampling, transformation, augmentation, and registration techniques.
Implementing PointNet architectures for classification and segmentation tasks.
3D CNN, 3D U-Net, Frustum PointNet, SparseConvNet, MinkowskiNet, KPConv.
Implementing pipelines using KITTI, SUN-RGBD, ModelNet, OpenPCDet.
Segmenting large-scale point cloud datasets using S3DIS, ScanNet.
Scene flow, instance segmentation, 3D reconstruction and SLAM overview.
3D GANs, VAEs, 3D Diffusion Models, and Large Image Models in 3D CV.
Applying pretrained models to new datasets and tasks. Model adaptation techniques.
Real-world case studies in robotics, healthcare, autonomous vehicles, and AR/VR.
End-to-end 3D CV + GenAI project with code, dataset, documentation, and review.
Select, clean, normalize, and handle noise on 3D datasets like ModelNet and ShapeNet.
Implement rotation, scaling, translation, and synthetic data generation to expand datasets.
Compute and visualize depth maps from stereo image pairs using DNN.
Build and evaluate models using 3D CNNs, PointNet, Frustum-PointNet for classification and localization.
Segment 3D scenes and objects using architectures like 3D U-Net and PointCNN for instance-aware labeling.
Build a system for analyzing 3D medical images for diagnostics and surgical assistance.
Reconstruct complete 3D models from 2D images or partial point cloud data using DNN.
Experiment with GANs and VAEs for synthetic 3D data generation and dataset enhancement.
Fine-tune pretrained 3D deep learning models for specialized computer vision tasks.
Successfully complete this comprehensive 3D Deep Learning for AI Computer Vision course and earn a Certificate that validates your expertise in advanced 3D AI computer vision techniques.