3D Reconstruction - Single Viewpoint Syllabus

This 8-week course provides a comprehensive introduction to reconstructing 3D scenes from a single 2D image, combining classical geometric methods with modern deep learning techniques. Each week requires approximately 6-8 hours of work, including lectures, readings, and assessments. The curriculum progresses from foundational concepts in camera geometry to advanced deep learning models for depth estimation, concluding with real-world applications in robotics and augmented reality.

Module 1: Introduction to 3D Vision

Estimated time: 12 hours

• Overview of 3D reconstruction challenges
• Perspective projection and camera models
• Depth cues in monocular images
• Applications of single-view 3D reconstruction

Module 2: Geometry and Camera Models

Estimated time: 12 hours

• Pinhole camera model and intrinsic parameters
• Extrinsic parameters and coordinate transformations
• Vanishing points and scene layout estimation
• Camera calibration techniques

Module 3: Deep Learning for Depth Estimation

Estimated time: 18 hours

• Neural network architectures for monocular depth prediction
• Training and evaluation datasets (e.g., NYU Depth, KITTI)
• Loss functions and supervision signals
• Feature extraction and encoder-decoder designs

Module 4: Applications and Evaluation

Estimated time: 6 hours

• 3D scene reconstruction pipelines
• Applications in AR, robotics, and autonomous navigation
• Quantitative and qualitative evaluation metrics

Module 5: Final Project

Estimated time: 10 hours

• Implement a monocular depth estimation model
• Evaluate 3D reconstruction accuracy on real images
• Interpret and visualize depth outputs in practical scenarios

Prerequisites

• Familiarity with linear algebra and matrix transformations
• Basic understanding of computer vision concepts
• Experience with Python and deep learning frameworks (e.g., PyTorch or TensorFlow)

What You'll Be Able to Do After

• Understand the principles of single-view 3D geometry and perspective projection
• Estimate depth and surface normals from a single image using deep learning
• Apply camera calibration and intrinsic parameters to reconstruct real-world scenes
• Use neural networks to infer 3D structure from 2D image inputs
• Evaluate reconstruction accuracy and interpret 3D outputs in practical applications