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Advanced 3D Deep Learning

Learn State-of-the art Advanced Deep Learning Models for Computer Vision applications.

Implement and Optimize models with Python, Pandas3D, OpenCV, Keras, PyTorch, PyTorch3D

In-depth knowledge with Quizzes, Assignments and Realtime Projects

Course Description

The Advanced 3D Deep Learning course dives into the frontier of modern computer vision and deep learning for 3D understanding. This course is designed for engineers, researchers, and developers aiming to build mastery in 3D data interpretation using cutting-edge neural techniques. You will gain hands-on knowledge of the most powerful models, architectures, and methods being used today to perceive, reconstruct, and analyze 3D environments and human-object interactions.

The curriculum begins with Monocular and Stereo Depth Estimation and progresses to complex tasks like Neural Scene Reconstruction and Neural Radiance Fields (NeRF). Students will explore deep learning models for surface and shape reconstruction, and move into advanced topics such as 3D human and object detection, pose estimation, activity recognition, and SLAM systems powered by deep visual odometry.

With a balanced blend of theoretical concepts and practical implementation, this course empowers learners to confidently apply neural networks in 3D space for solving real-world perception challenges in fields like robotics, AR/VR, autonomous navigation, surveillance, and interactive applications.

🌟 Course Highlights

📘 Modules: 14 research-backed modules tailored to advanced learners.
📚 Topics Covered: 100+ advanced topics from state-of-the-art 3D deep learning literature.
🛠️ Mini Applications: 10+ practical mini-projects spanning various domains.
🎥 Video Lectures: 35+ hours of expert-led video content with real-time walkthroughs.
🧪 Interactive Quizzes: Instant feedback to assess learning progress per module.
📝 Assignments: Structured hands-on exercises with real 3D datasets.
🚀 Capstone Project: Solve a complex 3D AI challenge from end to end.
🗂️ Curated Datasets: Use industry-standard datasets for real-world simulation.
🧑‍💻 Structured Jupyter Notebooks: Downloadable code notebooks for each core concept.
🎯 Tools Covered: PyTorch3D, Open3D, Kaolin, NeRF Studio, MeshIO, and more.
💻 Source Code: Full access to projects, mini apps, and model pipelines.
📜 Certificate of Completion: Verified certification with expert review and feedback.
🙋 Doubt Support: Get help via dedicated community forum or mentor sessions.
🎓 1:1 Mentorship: Book 1-on-1 sessions with an expert to guide your learning.
🧠 Interview Prep: Tips, mock questions, and job readiness for CV and 3D roles.
📊 Docs & PPTs: Detailed explainers and presentation slides.

📚 Course Modules Overview

Single Image Depth Estimation

Learn to estimate depth from monocular images using deep CNNs and self-supervised learning techniques.

Depth from Stereo

Understand stereo disparity estimation and end-to-end learning methods for accurate depth prediction.

3D Shape Estimation and Reconstruction

Explore implicit surface modeling and mesh reconstruction using adversarial and deep learning methods.

Learning 3D Object Shape

Train GANs, VAEs, and self-supervised models for generating and representing 3D object shapes.

Neural 3D Scene Reconstruction

Master neural representations and uncertainty modeling in end-to-end 3D scene reconstruction systems.

NERF (Neural Radiance Fields)

Build and apply deep NeRFs for 3D rendering, including multi-modal and enhanced NeRF models.

Point-NERF and Depth-NERF

Use point-based and depth-informed NeRFs for novel view synthesis and scene representation.

3D Human and Object Detection

Detect faces, humans, and objects in 3D using multi-view and occlusion-aware deep learning models.

3D Human and Object Tracking

Track facial features and objects in real-time 3D space using deep tracking and association networks.

3D Pose Estimation and Recognition

Estimate camera and object poses from RGB-D inputs using deep localization and invariant models.

3D Human Activity Recognition

Recognize human actions using skeleton data, temporal modeling, and multi-modal deep learning.

Simultaneous Localization and Mapping (SLAM)

Integrate deep learning into SLAM systems for feature extraction, loop closure, and visual odometry.

🧰 Tools & Libraries You’ll Master in This Course

Python
NumPy
OpenCV
Scikit-Learn
Albumentations
Keras
PyTorch3D
MediaPipe
+ 3D CV AI Libraries

🔍 Learning Outcomes

By the end of this course, you will be able to:

    • ✅ Build Strong Foundations in 3D Deep Learning Understand core principles behind monocular and stereo-based depth estimation using CNNs, self-supervised methods, and uncertainty modeling. Master techniques in disparity estimation, occlusion handling, and multi-view consistency.
 
    • 🧠 Gain Expertise in Advanced 3D Shape and Surface Reconstruction Apply deep implicit representations for surface estimation and mesh reconstruction using adversarial and generative techniques. Leverage generative models like GANs and VAEs to learn 3D object shapes from multi-view and self-supervised data.
 
    • 🎥 Reconstruct and Render Scenes with Neural Radiance Fields Implement neural scene reconstruction using NeRF and its state-of-the-art extensions such as Point-NeRF and Depth-NeRF. Analyze scene composition through learned radiance and geometry for photo-realistic rendering.
 
    • 🧍‍♂️ Perform Robust 3D Object Detection and Tracking Develop 3D detection systems for faces, humans, and general objects using multi-view and pose-guided deep learning models. Implement deep tracking algorithms for single and multiple objects, including real-time 3D face tracking.
 
    • 📍 Estimate Camera and Object Pose with Deep Learning Accurately estimate camera pose using deep localization and SLAM-based techniques for real-world environments. Extract object pose and human activity patterns using multi-modal fusion of RGB-D and sensor data.
 
    • 🗺️ Integrate Deep Learning into SLAM Systems Apply deep neural networks to traditional SLAM pipelines for better map fusion, global localization, and loop closure detection. Understand how learning-based SLAM improves robustness in complex, dynamic scenes.
 
    • 🛠️ Apply Tools and Frameworks for 3D Deep Learning Utilize essential tools like Python, NumPy, Open3D, PyTorch3D, and TensorFlow 3D for development and visualization. Gain hands-on experience with state-of-the-art libraries for 3D vision, reconstruction, and manipulation.
 
    • 💼 Execute Real-world Projects with Confidence Design and implement industry-relevant projects in depth estimation, shape learning, 3D detection, SLAM, and tracking. Build a portfolio of end-to-end solutions demonstrating your capabilities to employers and clients.
 
    • 🚀 Optimize and Deploy 3D Deep Learning Models Learn model optimization techniques for deploying on edge devices or cloud platforms. Prepare models for real-world production by balancing accuracy, speed, and resource constraints.
 
  • 📈 Align with Industry Demands and Job Roles Gain exposure to real-life applications in robotics, AR/VR, autonomous systems, and healthcare. Prepare for job roles such as 3D Vision Engineer, AI Researcher, or Autonomous Systems Developer with practical insights and coding fluency.

🧠 Assignments, Quizzes & Interview Overview

  • Regular Assignments: Practical coding and theoretical tasks designed to reinforce each module’s concepts and ensure hands-on learning.
  • Interactive Quizzes: Test your understanding immediately after every module to solidify knowledge and track progress.
  • Capstone & Mini Projects: Apply concepts in real-world inspired mini projects throughout the course and an extensive capstone project to showcase your skills.

Sample Project List

Monocular Depth Estimation CNN

Implement and experiment with architectures and training strategies to improve accuracy of single image depth prediction.

Stereo Depth Prediction Network

Develop end-to-end stereo depth models addressing occlusion and robustness challenges with deep CNNs.

3D Surface Reconstruction

Explore implicit surface representations and mesh reconstruction with adversarial training for high-fidelity 3D shapes.

3D Object Shape Generation

Use GANs and VAEs to generate 3D shapes, including self-supervised deep learning for robust object representations.

Neural Scene Reconstruction

Build end-to-end deep learning models for scene reconstruction from 2D images with uncertainty modeling.

NERF and Depth-NERF Rendering

Develop neural radiance fields projects for novel view synthesis using multi-modal and depth-conditional NERF models.

3D Face Detection and Recognition

Implement multi-view deep networks for 3D face detection, pose-guided models, and unconstrained detection techniques.

3D Human Detection and Tracking

Develop models for 3D human detection with multi-modal fusion and occlusion-aware deep learning techniques.

3D Object Tracking

Implement deep learning-based 3D object tracking and multi-object association for video sequences.

Camera Pose Estimation

Explore deep learning for camera localization, visual odometry, and SLAM for accurate pose estimation.

3D Object Pose Estimation

Implement pose estimation from RGB-D data with networks for object manipulation and viewpoint invariance.

3D Human Activity Recognition

Build models for skeleton-based action recognition, temporal convolutional networks, and multi-modal fusion.

Deep Learning in SLAM

Implement visual feature extraction, loop closure detection, and map fusion in SLAM enhanced with deep learning.

📜 Certificate of Completion

Upon successful completion of this advanced 3D deep learning course, you will receive a professionally designed certificate, verified by our experts. This certificate validates your skills and knowledge in cutting-edge 3D deep learning techniques and practical project experience — perfect for showcasing on LinkedIn, resumes, and portfolios.

Verified Credential

Project Evaluation

Industry Recognition

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