Feature points evaluation on omnidirectional vision with a photorealistic fisheye sequence -- A report on experiments done in 2014

TL;DR

This report evaluates feature detection and description methods on a new photorealistic fisheye image dataset to identify the most effective techniques for urban scene localization, focusing on calibration and visual odometry applications.

Contribution

It provides a new dataset, PFSeq, and a comprehensive experimental analysis of feature detection and description for fisheye images in urban environments.

Findings

Identified the most suitable feature detectors for fisheye images

Provided insights into feature descriptor performance in urban scenes

Established baseline results for future fisheye vision research

Abstract

What is this report: This is a scientific report, contributing with a detailed bibliography, a dataset which we will call now PFSeq for ''Photorealistic Fisheye Sequence'' and make available at https://doi.org/10. 57745/DYIVVU, and comprehensive experiments. This work should be considered as a draft, and has been done during my PhD thesis ''Construction of 3D models from fisheye video data-Application to the localisation in urban area'' in 2014 [Mor16]. These results have never been published. The aim was to find the best features detector and descriptor for fisheye images, in the context of selfcalibration, with cameras mounted on the top of a car and aiming at the zenith (to proceed then fisheye visual odometry and stereovision in urban scenes). We face a chicken and egg problem, because we can not take advantage of an accurate projection model for an optimal features detection and description, and we rightly need good features to perform the calibration (i.e. to compute the accurate projection model of the camera). What is not this report: It does not contribute with new features algorithm. It does not compare standard features algorithms to algorithms designed for omnidirectional images (unfortunately). It has not been peer-reviewed. Discussions have been translated and enhanced but the experiments have not been run again and the report has not been updated accordingly to the evolution of the state-of-the-art (read this as a 2014 report).