Table of Contents

1. 3D Computer Vision Fundamentals
   • Depth Estimation & Stereo Vision
   • Point Cloud Processing
   • 3D Reconstruction
2. Motion Analysis & Tracking
   • Optical Flow Techniques
   • Object Tracking Algorithms
   • Video Stabilization
3. Deep Learning for Computer Vision
   • Transfer Learning with Pretrained Networks
   • Object Detection with YOLO & Faster R-CNN
   • Semantic Segmentation
4. Comprehensive Final Project
   • Autonomous Vehicle Perception System
   • Industrial Quality Inspection Suite
   • Medical Image Analysis Pipeline
5. Performance Optimization & Deployment
   • GPU Acceleration Strategies
   • C/C++ Code Generation
   • Cloud Deployment Options
6. Next Steps & Advanced Resources

1. 3D Computer Vision Fundamentals

1.1 Stereo Depth Estimation

% Load stereo image pair
I1 = imread('scene_left.jpg');
I2 = imread('scene_right.jpg');

% Compute disparity map
disparityRange = [0 64];
disparityMap = disparitySGM(rgb2gray(I1), rgb2gray(I2),...
                'DisparityRange', disparityRange,...
                'UniquenessThreshold', 15);

% Visualize results
figure;
imshow(disparityMap, disparityRange);
colormap jet; colorbar;
title('Disparity Map');

Key Parameters:

• Disparity range adjustment
• Similarity metric selection (SSD, NCC)
• Post-processing filters

1.2 Point Cloud Processing

% Generate point cloud from depth data
ptCloud = pcfromdepth(depthImage, depthFactor, intrinsics);

% Downsample and denoise
ptCloud = pcdownsample(ptCloud, 'gridAverage', 0.01);
ptCloud = pcdenoise(ptCloud);

% Visualize with custom view
player = pcplayer(ptCloud.XLimits, ptCloud.YLimits, ptCloud.ZLimits);
view(player, ptCloud);

Applications: