3D Reconstruction with Low Resolution, Small Baseline and High Radial Distortion Stereo Images

TL;DR

This paper evaluates methods for 3D reconstruction using low-resolution, high-distortion stereo images from a small baseline system, focusing on radial distortion models to improve accuracy without heavy computation.

Contribution

It provides a comparative analysis of radial distortion models, especially the polynomial and division models, for 3D reconstruction from challenging stereo images.

Findings

Division model with a single parameter has limitations

Polynomial model effectively captures radial distortion

Approach achieves accurate reconstructions with low computational cost

Abstract

In this paper we analyze and compare approaches for 3D reconstruction from low-resolution (250x250), high radial distortion stereo images, which are acquired with small baseline (approximately 1mm). These images are acquired with the system NanEye Stereo manufactured by CMOSIS/AWAIBA. These stereo cameras have also small apertures, which means that high levels of illumination are required. The goal was to develop an approach yielding accurate reconstructions, with a low computational cost, i.e., avoiding non-linear numerical optimization algorithms. In particular we focused on the analysis and comparison of radial distortion models. To perform the analysis and comparison, we defined a baseline method based on available software and methods, such as the Bouguet toolbox [2] or the Computer Vision Toolbox from Matlab. The approaches tested were based on the use of the polynomial model of radial distortion, and on the application of the division model. The issue of the center of distortion was also addressed within the framework of the application of the division model. We concluded that the division model with a single radial distortion parameter has limitations.