Headcam: Cylindrical Panoramic Video Dataset for Unsupervised Learning of Depth and Ego-Motion

This dataset contains panoramic video captured from a helmet-mounted camera while riding a bike through suburban Northern Virginia. We used the videos to evaluate an unsupervised learning method for depth and ego-motion estimation, as described in our paper: Alisha Sharma and Jonathan Ventura. "Unsupervised Learning of Depth and Ego-Motion from Cylindrical Panoramic Video." Proceedings of the 2019 IEEE Artificial Intelligence & Virtual Reality Conference, San Diego, CA, 2019. If you make use of this dataset, please cite this paper. The videos are stored as .mkv video files encoded using lossless H.264. To extract the images, we recommend using ffmpeg: mkdir 2018-10-03 ; ffmpeg -i 2018-10-03.mkv -q:v 1 2018-10-03/%05d.png ; Associated code can be found in our GitHub repository.本数据集包含由头盔式相机（helmet-mounted camera）在北弗吉尼亚州郊区骑行过程中采集的全景视频。本数据集用于评估一种面向深度与自运动估计的无监督学习方法，相关细节见于我们发表于2019年IEEE人工智能与虚拟现实会议的论文：Alisha Sharma与Jonathan Ventura所著《基于圆柱全景视频的深度与自运动无监督学习》，该会议于2019年在加利福尼亚州圣地亚哥举行。若您使用本数据集，请引用该论文。所有视频均采用无损H.264编码，存储为.mkv格式文件。若需提取图像帧，我们推荐使用FFmpeg工具，示例命令如下：mkdir 2018-10-03 ; ffmpeg -i 2018-10-03.mkv -q:v 1 2018-10-03/%05d.png。相关代码可在我们的GitHub仓库中获取。

搜集汇总

背景与挑战

背景概述

Headcam数据集是一个用于深度和自运动估计无监督学习的圆柱形全景视频数据集，包含从头盔摄像头在郊区自行车骑行中捕获的视频，总大小为44.8 GB，以MKV格式存储，并附带相关论文和代码资源。