Depth Estimation maps of lidar and stereo images

TL;DR

This paper evaluates and compares depth estimation methods using lidar data and stereo images, focusing on performance, optimization, and the rationale for using pure mathematics over machine learning.

Contribution

It provides a detailed performance analysis of depth maps from stereo images and lidar, and discusses optimization techniques and the choice of mathematical methods over machine learning.

Findings

Stereo image-based depth estimation methods evaluated

Lidar data used for depth estimation analyzed

Optimization techniques improve depth map accuracy

Abstract

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.