论文标题
LIDAR和立体声图像的深度估计图
Depth Estimation maps of lidar and stereo images
论文作者
论文摘要
作为技术报告，本文着重于基于激光雷达数据和立体声图像的深度估计的评估和性能（左前和右前）。 LIDAR 3D云数据和立体声图像由福特提供。此外，本文还将解释有关深度估计性能优化的一些详细信息。以及为什么不使用机器学习进行深度估计的一些原因，取而代之的是纯数学来进行立体声深度估计。本文的结构由以下以下方式制成：（1）绩效：讨论和评估从立体声图像和3D云点创建的深度图，以及对一致性和错误的关系分析；（2）通过立体声图像进行深度估算；以说明如何使用STEREO图像来估算估算方法，以解释如何使用lidar;（3）概述的方法：报告主要是为了显示深度图的性能及其方法，并为其分析。
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.
