---
title: Lecture - Real World Reconstruction
---

**Reading** Ma 2004:Ch 11

# Overview

1.  [Feature Detection](#fd)
2.  [Feature Correspondence](#fc)
3.  [Projective Reconstruction](#pr)
3.  [Euclidean Reconstruction](#er)

# Feature Detection {#fd}

+ Harris Detector
+ Tiling
    - divide the image into, say, $10\times10$ tiles
    - select features per tile
+ Separation
    - one feature may cause several pixels to be marked as a corner
    - separation should be larger than the windows size used in detection
+ Sorting by strength
    - sort first, and then select from top of the list
    - enforce separation from previously selected features

# Feature Correspondence {#fc}

+ Small Baseline (motion video)
    - feature tracking - calculate motion
+ Moderate Baseline (snapshots)
+ Wide Baseline -> use SIFT or similar methods
    - the textbook is outdated on this point

## Multiscale iterative feature tracking

**TODO**


# Projective Reconstruction {#pr}

# Euclidean Reconstruction {#er}