2014年实验对比鱼眼图像特征检测器，为车载鱼眼视觉里程计提供选型参考。
Feature points evaluation on omnidirectional vision with a photorealistic fisheye sequence -- A report on experiments done in 2014
- 基于真实鱼眼序列测试主流特征检测与描述子性能。
- 发现SIFT在鱼眼图像中仍具较好稳定性与匹配精度。
- 适合做车载鱼眼视觉定位与建模的研究者参考。
本报告是2014年博士课题的一部分，旨在评估鱼眼图像中最佳的特征检测器与描述子，用于车载鱼眼相机自标定及城市环境下的视觉里程计与立体视觉。由于缺乏精确投影模型，无法优化特征提取，而高精度特征又需校准模型支持，形成“鸡生蛋、蛋生鸡”难题。报告提供详尽文献综述、名为PFSeq（Photorealistic Fisheye Sequence）的可公开数据集（DOI: 10.57745/DYIVVU），并开展全面实验。未提出新算法，也未对比专为全景设计的算法，且未经同行评审。结果反映当时技术状况，不代表当前最新水平。
原文摘要 · Abstract (English)
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 It has not been peer-reviewed.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).
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