This paper as technology report is focusing on evaluation and performance about depth estimations based on lidar data and stereo images(front left and front right). The lidar 3d cloud data and stereo images are provided by ford. In addition, this paper also will explain some details about optimization for depth estimation performance. And some reasons why not use machine learning to do depth estimation, replaced by pure mathmatics to do stereo depth estimation. The structure of this paper is made of by following:(1) Performance: to discuss and evaluate about depth maps created from stereo images and 3D cloud points, and relationships analysis for alignment and errors;(2) Depth estimation by stereo images: to explain the methods about how to use stereo images to estimate depth;(3)Depth estimation by lidar: to explain the methods about how to use 3d cloud datas to estimate depth;In summary, this report is mainly to show the performance of depth maps and their approaches, analysis for them.
翻译:本文作为技术报告,侧重于根据立体声数据和立体声图像(左前和右前)对深度估计的评价和业绩。Lidar 3d云数据和立体声图像由福特提供。此外,本文还将解释关于深度估计性能优化的一些细节。以及为什么不使用机器学习进行深度估计,代之以纯数学来进行立体声深度估计的一些原因。本文的结构如下:(1) 性能:讨论和评价立体声图像和3D云点产生的深度地图,以及用于校正和误差的关系分析;(2) 立体声图像的深度估计:解释如何使用立体声图像来估计深度的方法;(3) 里达估算:解释如何使用3D云数据来估计深度的方法;摘要,本报告主要展示深度地图的性能及其方法,并为它们进行分析。