# Comparing two- and three-view Computer Vision

## TL;DR

This paper compares two-view and three-view computer vision methods for 3D reconstruction, analyzing how camera parameters affect point reconstruction quantity and dispersion, revealing trade-offs between the methods.

## Contribution

It provides a comparative analysis of two-view and three-view reconstruction methods, highlighting their performance differences under various camera settings.

## Key findings

- Two-view method reconstructs more points.
- Three-view method yields less dispersed points.
- Both methods perform similarly with optimal camera parameters.

## Abstract

To reconstruct the points in three dimensional space, we need at least two images. In this paper we compared two different methods: the first uses only two images, the second one uses three. During the research we measured how camera resolution, camera angles and camera distances influence the number of reconstructed points and the dispersion of them. The paper presents that using the two-view method, we can reconstruct significantly more points than using the other one, but the dispersion of points is smaller if we use the three-view method. Taking into consideration the different camera settings, we can say that both the two- and three-view method behaves the same, and the best parameters are also the same for both methods.