Summary of Analysis Of Different Disparity Estimation Techniques on Aerial Stereo Image Datasets, by Ishan Narayan et al.
Analysis of different disparity estimation techniques on aerial stereo image datasets
by Ishan Narayan, Shashi Poddar
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper analyzes the performance of various techniques for dense stereo correspondence analysis on aerial images. Traditional methods, optimization-based methods, and learning-based methods were implemented and compared to find the most effective approach for depth estimation from stereo images. The study used two stereo aerial datasets to evaluate different cost functions and techniques. Existing pre-trained models were also tested with various cost functions in Stereo SGBM. The results were compared using metrics such as MSE, SSIM, and others. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to find matching points between old and new pictures taken from the air. They tried different ways of doing this and saw which one worked best on aerial images. They used two special sets of pictures to test different methods and cost functions. They even tested some pre-trained models with these aerial images. The results were compared using measures like mean squared error, structural similarity index, and more. |
Keywords
» Artificial intelligence » Depth estimation » Mse » Optimization
