Hands-on 3D Computer Vision for Developers

Dive into the real-world of 3D vision with this practical course designed for developers.
Learn to capture, process, reconstruct, and analyze 3D data using cutting-edge techniques and tools.
From stereo vision to 3D deep learning — build hands-on expertise through project-driven learning.

Implement and Optimize algorithms with OpenCV, Open3D, PyntCloud, MeshIO, MeshLab

In-depth knowledge with Quizzes, Assignments and Realtime Projects

📘 Course Description

“Hands-On 3D Computer Vision for Developers” is a project-focused course designed to empower software engineers, AI/ML developers, and researchers with the practical skills needed to work with 3D vision systems. This comprehensive program covers the end-to-end workflow of 3D computer vision — from image-based 3D reconstruction to advanced deep learning applications in robotics, AR/VR, and autonomous systems.

The course begins with the foundations of 3D geometry, camera modeling, and transformation mathematics. You’ll learn to calibrate cameras, correct lens distortion, and understand intrinsic and extrinsic camera parameters. Moving forward, we’ll explore stereo vision, depth map creation, and building 3D data representations like point clouds and meshes.

In the advanced phase, you’ll dive into multi-view reconstruction using Structure-from-Motion (SfM), Multi-View Stereo (MVS), and volumetric reconstruction techniques. You’ll also gain exposure to 3D deep learning concepts and apply convolutional networks to 3D data for classification, segmentation, and scene understanding.

The final modules bring everything together with real-world case studies in medical imaging, robotics, and autonomous navigation. You’ll build industry-inspired projects that mimic real workflows, using libraries like OpenCV, Open3D, and PyTorch, while learning how to evaluate 3D vision pipelines for production-readiness.

By the end of this course, you’ll not only understand the theory behind 3D computer vision — you’ll have the hands-on experience needed to build robust, scalable 3D vision systems, optimized for real-world deployment.

📚 Course Modules Overview

Module 1: Introduction to 3D Computer Vision

Overview of 3D Computer Vision
Fundamentals of 3D Geometry and Transformations
Camera Models and Calibration

Module 2: Basic 3D Imaging and Reconstruction

Stereo Vision and Depth Perception
Essential and Fundamental Matrices
Point Clouds and 3D Data Representation

Module 3: 3D Reconstruction Techniques

Surface Reconstruction and Mesh Generation
3D Point Cloud Applications: Classification, Object Detection, Segmentation
3D Reconstruction Techniques

Module 4: Advanced Topics in 3D Computer Vision

Explore deep learning for 3D vision, convolutional networks for point clouds, and advanced 3D scene understanding.

Module 5: Real-world Applications and Case Studies

3D Vision in Robotics and Autonomous Systems
Medical Imaging and 3D Vision Applications

🌟 Course Highlights

📘 Modules: 12 well-structured modules

📚 Topics Covered: 140+ practical CV & AI topics

🛠️ Mini Applications: 15 real-world mini projects

🎥 Video Lectures: 45+ hours of high-quality instruction

🧪 Interactive Quizzes after each module

📝 Assignments to reinforce concepts

🚀 Project: Build an industry-demanding AI project

🗂️ Curated Datasets for hands-on practice

🧑‍💻 Structured Jupyter Notebooks for every topic

🎯 Industry-Relevant Tools: Python, OpenCV, PyTorch, Keras, Albumentations…

💻 Source Code Access to all projects and mini-apps

📜 Certificate of Completion with project review

🙋 Doubt Clearing Support via forum/email (or live sessions)

🎓 1:1 Mentorship Option (on booking based)

🧠 Interview Preparation Tips at the end of the track

📊 Set of PPTs & 📄 Explainable Docs

🎯 Learning Outcomes

📘 Module 1: Introduction to 3D Computer Vision ✔️ 3D fundamentals & real-world significance ✔️ Geometric transformations, coordinate systems ✔️ Camera models & calibration basics

🔍 Module 2: Basic 3D Imaging & Reconstruction ✔️ Stereo vision, depth perception & maps ✔️ Essential matrices & rectification ✔️ Point cloud basics & 3D data formats

🛠️ Module 3: 3D Reconstruction Techniques ✔️ Surface & mesh generation ✔️ Registration, segmentation & extraction ✔️ 3D reconstruction from images

🧠 Module 4: Advanced 3D Vision ✔️ Deep learning in 3D (CNNs, scene understanding) ✔️ AI for complex 3D vision tasks

🌐 Module 5: Real-world Applications ✔️ Robotics & autonomous systems ✔️ Navigation case studies ✔️ Medical imaging applications

🧰 Tool Proficiency ✔️ Python, NumPy, OpenCV, PIL ✔️ Matplotlib, Open3D, PyntCloud, MeshIO

⚙️ Practical Implementation ✔️ Hands-on with OpenCV, PIL, Scikit-learn ✔️ Real-world vision algorithms

Projects & Practice ✔️ Stereo vision, depth, 3D reconstruction ✔️ Object detection & segmentation projects

⚡ Optimization Strategies ✔️ Improve algorithm performance ✔️ Code-level efficiency for 3D pipelines

📈 Industry Best Practices ✔️ Stay current with trends & tools ✔️ Align learning with real-world standards

📚 Course Relevance ✔️ Developed by industry practitioners ✔️ Practical quizzes & assessments

🏁 Final Outcome ✔️ Implement full 3D vision projects ✔️ Confidently solve real-world challenges

🧰 Tools & Libraries You’ll Master in This Course

Python

Numpy

Matplotlib

OpenCV

Open3D

PyntCloud

MeshIO

MeshLab

+Other 3D CV libraries

🧠 Assignments, Quizzes, Projects & Interview Questions Overview

✅ Module-wise Assignments: Practice real-world 3D CV tasks after every module.

🧪 Interactive Quizzes: Test conceptual clarity and technical understanding.

🧠 Interview Prep: Includes curated 3D computer vision coding and theory questions.

Sample Project List

📌 Stereo Image Depth Estimation Generate depth maps from stereo image pairs using fundamental and essential matrices.

📌 3D Object Reconstruction from Images Use SfM to reconstruct 3D shapes from 2D views.

📌 3D Object Recognition with Point Clouds Detect and classify objects in raw point cloud data.

📌 3D Scene Reconstruction from Multiple Views Reconstruct large-scale scenes using SfM and MVS techniques.

📌 Point Cloud Segmentation Tool Isolate and label components in a 3D scan using clustering and projection.

📌 Point Cloud Registration System Align and merge point clouds using ICP, RANSAC, and global methods.

🎓 Certification – Showcase Your Expertise!

Upon completing this course, you’ll receive a professionally verified Certificate of Completion with your name, key skills, and completed project titles. It’s perfect for enhancing your LinkedIn profile, adding credibility to your resume, or demonstrating your technical abilities to recruiters and hiring managers.