The Sampling-Gaussian for stereo matching
<|reference_start|>The Sampling-Gaussian for stereo matching: 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.<|reference_end|>
@article{pan2024the,
    title={The Sampling-Gaussian for stereo matching},
    author={Baiyu Pan and jichao jiao and Bowen Yao and Jianxin Pang and Jun Cheng},
    journal={arXiv preprint arXiv:2410.06527},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.06527},
    primaryClass={cs.CV cs.AI}
}