# Confidence Inference for Focused Learning in Stereo Matching

arXiv: https://arxiv.org/abs/1809.09758 · HTML: https://mlsanity.com/paper/1809.09758/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2018.

## Built on by (most cited first, 1 of 1)

- [Revisiting Matching Response and Swept Feature Volumes for Wide-baseline Omnidirectional Stereo](https://mlsanity.com/paper/2607.11097/) (2026)

## Abstract (arXiv)

In this paper, we present confidence inference approachin an unsupervised way in stereo matching. Deep Neu-ral Networks (DNNs) have recently been achieving state-of-the-art performance. However, it is often hard to tellwhether the trained model was making sensible predictionsor just guessing at random. To address this problem, westart from a probabilistic interpretation of theL1loss usedin stereo matching, which inherently assumes an indepen-dent and identical (aka i.i.d.) Laplacian distribution. Weshow that with the newly introduced dense confidence map,the identical assumption is relaxed. Intuitively, the vari-ance in the Laplacian distribution is large for low confidentpixels while small for high-confidence pixels. In practice,the network learns toattenuatelow-confidence pixels (e.g.,noisy input, occlusions, featureless regions) andfocusonhigh-confidence pixels. Moreover, it can be observed fromexperiments that the focused learning is very helpful in find-ing a better convergence state of the trained model, reduc-ing over-fitting on a given dataset.