Depth Estimation Analysis of Orthogonally Divergent Fisheye Cameras with Distortion Removal

TL;DR

This paper introduces a novel distortion-removal and depth estimation method for stereo vision systems using orthogonally divergent fisheye cameras, employing virtual pinhole cameras to improve accuracy in 3D reconstruction.

Contribution

It presents a new approach that uses virtual pinhole cameras to mitigate fisheye distortion and enhance depth estimation in orthogonally divergent fisheye stereo systems.

Findings

Effective distortion removal demonstrated in both simulation and real-world experiments.

Improved depth estimation accuracy compared to traditional fisheye stereo setups.

Method suitable for applications requiring wide field-of-view 3D perception.

Abstract

Stereo vision systems have become popular in computer vision applications, such as 3D reconstruction, object tracking, and autonomous navigation. However, traditional stereo vision systems that use rectilinear lenses may not be suitable for certain scenarios due to their limited field of view. This has led to the popularity of vision systems based on one or multiple fisheye cameras in different orientations, which can provide a field of view of 180x180 degrees or more. However, fisheye cameras introduce significant distortion at the edges that affects the accuracy of stereo matching and depth estimation. To overcome these limitations, this paper proposes a method for distortion-removal and depth estimation analysis for stereovision system using orthogonally divergent fisheye cameras (ODFC). The proposed method uses two virtual pinhole cameras (VPC), each VPC captures a small portion of the original view and presents it without any lens distortions, emulating the behavior of a pinhole camera. By carefully selecting the captured regions, it is possible to create a stereo pair using two VPCs. The performance of the proposed method is evaluated in both simulation using virtual environment and experiments using real cameras and their results compared to stereo cameras with parallel optical axes. The results demonstrate the effectiveness of the proposed method in terms of distortion removal and depth estimation accuracy.