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A quantitative evaluation of confidence measures for stereo vision
Xiaoyan Hu1, Philippos Mordohai
1Department of Computer Science, Stevens Institute of Technology, Castle Point on Hudson, Hoboken, NJ 07030, USA. xhu2@stevens.edu
IEEE Transactions on Pattern Analysis and Machine Intelligence
|February 15, 2012
Summary
This study evaluates 17 stereo matching confidence measures, comparing established and novel methods. Findings aid researchers in selecting optimal techniques for accurate depth map generation and occluded pixel detection in computer vision.
Area of Science:
- Computer Vision
- Machine Learning
- 3D Reconstruction
Background:
- Stereo matching is crucial for 3D scene understanding.
- Existing confidence measures for stereo matching lack comprehensive evaluation.
- Novel techniques are needed to improve depth estimation accuracy.
Purpose of the Study:
- To extensively evaluate 17 confidence measures for stereo matching.
- To categorize and compare existing and novel confidence estimation methods.
- To provide guidance for selecting effective confidence measures in stereo vision.
Main Methods:
- Categorization of confidence measures based on stereo cost estimation.
- Winner-take-all framework evaluation on binocular and multi-baseline datasets.
- Assessment of ranking, occlusion detection, and hypothesis selection capabilities.
Main Results:
- Comparative analysis of 17 confidence measures' strengths and weaknesses.
- Identification of top-performing methods for depth estimation accuracy.
- Evaluation of occlusion detection and multi-hypothesis selection performance.
Conclusions:
- A comprehensive evaluation framework for stereo matching confidence measures is established.
- Findings offer practical insights for researchers in binocular and multi-view stereo.
- The study highlights the importance of rigorous evaluation for advancing stereo vision techniques.
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