Tuning of Adaptive Weight Depth Map Generation Algorithms

In depth map generation algorithms, parameters settings to yield an accurate disparity map estimation are usually chosen empirically or based on unplanned experiments. Algorithms’ performance is measured based on the distance of the algorithm results vs. the Ground Truth by Middlebury’s standards. This work shows a systematic statistical approach including exploratory data analyses on over 14000 images and designs of experiments using 31 depth maps to measure the relative influence of the parameters and to fine-tune them based on the number of bad pixels. The implemented methodology improves the performance of adaptive weight based dense depth map algorithms. As a result, the algorithm improves from 16.78 to 14.48 % bad pixels using a classical exploratory data analysis of over 14000 existing images, while using designs of computer experiments with 31 runs yielded an even better performance by lowering bad pixels from 16.78 to 13 %.