Low-res two-view benchmark
This table lists the benchmark results for the low-res two-view scenario. The benchmark evaluates the Middlebury stereo metrics:
- bad 0.5, 1.0, 2.0, 4.0 [%]: Fraction of pixels with errors larger than the given number of disparities.
- Average error [px]: The per-pixel average disparity error.
- Root mean square error [px]: The per-pixel root mean square disparity error.
- 50%, 90%, 95%, 99% error quantile [px]: The highest disparity error within the given percentage of best pixels (for 50%, this is the median error).
- Time [s]: The runtime of the method.
The mask determines whether the metric is evaluated for all pixels with ground truth, or only for pixels which are visible in both images (non-occluded).
The coverage selector allows to limit the table to results for all pixels (dense), or a given minimum fraction of pixels.
Click one or more dataset result cells or column headers to show visualizations. Most visualizations are only available for training datasets. The visualizations may not work with mobile browsers.
Since we plan to add additional datasets soon (~ end of September), which will likely change the ranking, the average scores are currently still hidden.