Stereo matching has emerged as a cost-effective solution for road surface 3D reconstruction, garnering significant attention towards improving both computational efficiency and accuracy. This article introduces decisive disparity diffusion (D3Stereo), marking the first exploration of dense deep feature matching that adapts pre-trained deep convolutional neural networks (DCNNs) to previously unseen road scenarios. A pyramid of cost volumes is initially created using various levels of learned representations. Subsequently, a novel recursive bilateral filtering algorithm is employed to aggregate these costs. A key innovation of D3Stereo lies in its alternating decisive disparity diffusion strategy, wherein intra-scale diffusion is employed to complete sparse disparity images, while inter-scale inheritance provides valuable prior information for higher resolutions. Extensive experiments conducted on our created UDTIRI-Stereo and Stereo-Road datasets underscore the effectiveness of D3Stereo strategy in adapting pre-trained DCNNs and its superior performance compared to all other explicit programming-based algorithms designed specifically for road surface 3D reconstruction. Additional experiments conducted on the Middlebury dataset with backbone DCNNs pre-trained on the ImageNet database further validate the versatility of D3Stereo strategy in tackling general stereo matching problems.

翻译：立体匹配已成为道路表面三维重建的一种经济高效解决方案，在提升计算效率与精度方面受到广泛关注。本文提出了决定性视差扩散方法（D3Stereo），首次探索了将预训练深度卷积神经网络（DCNNs）适配到未见道路场景的密集深度特征匹配技术。该方法首先利用多层级学习表征构建代价体金字塔，随后采用新颖的递归双边滤波算法进行代价聚合。D3Stereo的核心创新在于其交替式决定性视差扩散策略：尺度内扩散用于补全稀疏视差图，而尺度间继承则为更高分辨率提供有价值的先验信息。在我们构建的UDTIRI-Stereo和Stereo-Road数据集上进行的大量实验表明，D3Stereo策略在适配预训练DCNNs方面具有显著效果，其性能优于所有其他专门针对道路表面三维重建的显式编程算法。在Middlebury数据集上使用ImageNet数据库预训练骨干DCNNs进行的补充实验，进一步验证了D3Stereo策略在处理通用立体匹配问题时的广泛适用性。