The Sampling-Gaussian for stereo matching

Baiyu Pan, jichao jiao, BowenYao, Jianxin Pang, Jun Cheng

Keywords: stereo matching.+disparity estimation.+Gaussian Distribution.+interpretable deep learning

TL;DR: A novel Gaussian distribution-based supervision method for stereo matching. Implemented with five baseline methods and achieves notable improvement.

Abstract: The soft-argmax operation is widely adopted in neural network-based stereo matching methods to enable differentiable regression of disparity. However, networks trained with soft-argmax tend to predict multimodal probability distributions due to the absence of explicit constraints on the shape of the distribution. Previous methods leveraged Laplacian distributions and cross-entropy for training but failed to effectively improve accuracy and even increased the network's processing time. In this paper, we propose a novel method called Sampling-Gaussian as a substitute for soft-argmax. It improves accuracy without increasing inference time. We innovatively interpret the training process as minimizing the distance in vector space and propose a combined loss of L1 loss and cosine similarity loss. We leveraged the normalized discrete Gaussian distribution for supervision. Moreover, we identified two issues in previous methods and proposed extending the disparity range and employing bilinear interpolation as solutions. We have conducted comprehensive experiments to demonstrate the superior performance of our Sampling-Gaussian method. The experimental results prove that we have achieved better accuracy on five baseline methods across four datasets. Moreover, we have achieved significant improvements on small datasets and models with weaker generalization capabilities. Our method is easy to implement, and the code is available online.

Primary Area: interpretability and explainable AI

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.

Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Submission Number: 11209