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 It has not been peer-reviewed.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).

翻译：报告性质说明：本科学报告提供详尽的参考文献、一套现命名为"PFSeq"（逼真鱼眼序列）的数据集（可通过https://doi.org/10.57745/DYIVVU获取）以及完整的实验分析。本工作应视为草稿版本，完成于2014年笔者博士论文《基于鱼眼视频数据的三维模型构建——城市区域定位应用》研究期间[Mor16]，相关成果从未正式发表。研究目标是在车载顶置朝向天顶的相机自标定背景下，探寻适用于鱼眼图像的最佳特征检测与描述方法（旨在实现城市场景中的鱼眼视觉里程计与立体视觉）。我们面临"鸡与蛋"的悖论：一方面无法依赖精确投影模型实现最优特征检测与描述，另一方面又需要优质特征来完成相机标定（即计算相机的精确投影模型）。非报告范畴说明：本研究未提出新的特征算法；未将标准特征算法与专为全向图像设计的算法进行比较（此为缺憾）；未经同行评审。讨论部分经过翻译与润色，但实验未重新执行，报告内容亦未根据技术发展现状进行更新（请将其视为2014年的历史报告）。