Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching

Understand

State-of-the-art deep learning based stereo matching approaches treat disparity estimation as a regression problem, where loss function is directly defined on true disparities and their estimated ones.

• However, disparity is just a byproduct of a matching process modeled by cost volume, while indirectly learning cost volume driven by disparity regression is prone to overfitting since the cost volume is under constrained.
• In this paper, we propose to directly add constraints to the cost volume by filtering cost volume with unimodal distribution peaked at true disparities.
• In addition, variances of the unimodal distributions for each pixel are estimated to explicitly model matching uncertainty under different contexts.