The Sampling-Gaussian for stereo matching

• URL: http://arxiv.org/abs/2410.06527v1
• Date: Wed, 9 Oct 2024 03:57:13 GMT
• Title: The Sampling-Gaussian for stereo matching
• Authors: Baiyu Pan, jichao jiao, Bowen Yao, Jianxin Pang, Jun Cheng,
• Abstract summary: The soft-argmax operation is widely adopted in neural network-based stereo matching methods. Previous methods failed to effectively improve the accuracy and even compromises the efficiency of the network. We propose a novel supervision method for stereo matching, Sampling-Gaussian.
• Score: 7.9898209414259425
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The soft-argmax operation is widely adopted in neural network-based stereo matching methods to enable differentiable regression of disparity. However, network trained with soft-argmax is prone to being multimodal due to absence of explicit constraint to the shape of the probability distribution. Previous methods leverages Laplacian distribution and cross-entropy for training but failed to effectively improve the accuracy and even compromises the efficiency of the network. In this paper, we conduct a detailed analysis of the previous distribution-based methods and propose a novel supervision method for stereo matching, Sampling-Gaussian. We sample from the Gaussian distribution for supervision. Moreover, we interpret the training as minimizing the distance in vector space and propose a combined loss of L1 loss and cosine similarity loss. Additionally, we leveraged bilinear interpolation to upsample the cost volume. Our method can be directly applied to any soft-argmax-based stereo matching method without a reduction in efficiency. We have conducted comprehensive experiments to demonstrate the superior performance of our Sampling-Gaussian. The experimental results prove that we have achieved better accuracy on five baseline methods and two datasets. Our method is easy to implement, and the code is available online.

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