用采样高斯分布提升立体匹配精度，无需牺牲效率。
The Sampling-Gaussian for stereo matching
- 从高斯分布采样进行监督，替代传统软argmax的约束方式。
- 在五个基线方法和两个数据集上均实现更优精度，误差更低。
- 可无缝集成到现有方法中，代码开源，适合工程落地。
软argmax操作广泛用于基于神经网络的立体匹配方法中，以实现视差的可微回归。然而，仅使用软argmax训练的网络容易出现多模态问题，因概率分布形状缺乏显式约束。此前方法采用拉普拉斯分布与交叉熵进行训练，但未能有效提升精度，反而影响网络效率。本文深入分析了基于分布的训练方法，提出一种新的立体匹配监督策略——Sampling-Gaussian：通过从高斯分布中采样进行监督，并将训练理解为向量空间距离最小化，结合L1损失与余弦相似度损失。此外，我们采用双线性插值上采样代价体积。该方法可直接应用于任意基于软argmax的立体匹配方法，且不降低效率。大量实验表明，我们的方法在五个基线模型和两个数据集上均取得更优性能，验证了其有效性。代码已公开。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。