Stereo-Matching in the Context of Vision-Augmented Vehicles
Stereo matching accuracy is determined by comparing results with ground truth. However, the kind of detail remains unspecified in regions where a stereo matcher is more accurate. By identifying feature points we are identifying regions where the data cost used can easily represent features. We suggest to use feature matchers for identifying sparse matches of high confidence, and to use those for guiding a belief-propagation mechanism.
Extensive experiments, also including a semi-global stereo matcher, illustrate achieved performance. We also test on data just recently made available for a developing country, which comes with particular challenges not seen before. Since KITTI ground truth is sparse, for most of identified feature points ground truth is actually missing. By using our novel stereo matching method (called FlinBPM) we derive our own ground truth and compare it with results obtained by other matching approaches including our novel stereo matching method (called WlinBPM).
Based on this we were able to identify circumstances in which a census transform fails to define an appropriate data cost measure. There is not a single all-time winner in the set of considered stereo matchers, but there are specific benefits when applying one of the discussed stereo matching strategies. This might point towards a need of adaptive solutions for vision-augmented vehicles.
KeywordsFeature Point Pixel Location Stereo Matcher Outlier Removal Sparse Feature
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