对比多种立体匹配方法在航拍图像上的深度估计表现
Analysis of different disparity estimation techniques on aerial stereo image datasets
- 采用SGBM框架，测试不同代价函数在航拍图上的效果
- 在两个航拍数据集上，学习型方法优于传统与优化方法
- 适合关注航拍立体匹配与深度估计的科研人员
随着航拍图像数据集的出现，密集立体匹配取得了显著进展。本文分析了在航拍图像上使用不同技术进行密集立体对应关系的方法。传统方法、基于优化的方法以及基于学习的方法均被实现并对比。对于传统方法，采用Stereo SGBM架构，通过不同代价函数评估其在航拍数据集上的表现。尽管多数方法在标准数据集上表现良好，但在航拍数据集上缺乏充分基准测试。为此，对两个航拍立体图像数据集进行了视觉定性和定量分析，以比较不同代价函数和方法在深度估计中的性能。同时，利用现有预训练模型，测试了近期基于学习的架构在立体图像对上的表现，并结合SGBM中不同代价函数进行对比。输出结果与真实值通过均方误差（MSE）、结构相似性（SSIM）等误差指标进行评估。
原文摘要 · Abstract (English)
With the advent of aerial image datasets, dense stereo matching has gained tremendous progress. This work analyses dense stereo correspondence analysis on aerial images using different techniques. Traditional methods, optimization based methods and learning based methods have been implemented and compared here for aerial images. For traditional methods, we implemented the architecture of Stereo SGBM while using different cost functions to get an understanding of their performance on aerial datasets. Analysis of most of the methods in standard datasets has shown good performance, however in case of aerial dataset, not much benchmarking is available. Visual qualitative and quantitative analysis has been carried out for two stereo aerial datasets in order to compare different cost functions and techniques for the purpose of depth estimation from stereo images. Using existing pre-trained models, recent learning based architectures have also been tested on stereo pairs along with different cost functions in SGBM. The outputs and given ground truth are compared using MSE, SSIM and other error metrics.
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