Loading...
Recovering depth information from images using stereo vision, multi-view geometry, and deep learning approaches.
Depth is the missing dimension. Cameras project 3D scenes onto 2D images, losing depth information. This chapter explores how to recover it—from stereo cameras, multiple views, or even single images using deep learning.
What is this chapter about? We study the mathematics and algorithms for recovering depth from images. From classical stereo matching to modern neural networks, we cover the full spectrum of depth estimation techniques.
Why does this matter? Depth enables:
How the topics connect: We start with epipolar geometry—the mathematical constraints between two views. Stereo matching uses these constraints to find correspondences. Multi-view stereo extends to many images. Finally, deep depth estimation learns to predict depth from single images.
Click any topic to jump in
The geometric constraint between two views that reduces stereo search from 2D to 1D — the mathematical backbone of depth estimation.
Finding pixel correspondences along epipolar lines to compute disparity maps — the classical approach to dense depth.
Multi-view and learned approaches
Aggregating depth from many views for complete, accurate 3D reconstruction — handling occlusions through redundancy.
Neural networks predicting depth from single or paired images — learning geometric priors that bypass explicit matching.
This chapter is part of PixelBank Premium. Create a free account, then upgrade to read the full lesson — concepts, walkthroughs, and exercises.