Stereo matching is vital in 3D computer vision, with most algorithms assuming symmetric visual properties between binocular visions. However, the rise of asymmetric multi-camera systems (e.g., tele-wide cameras) challenges this assumption and complicates stereo matching. Visual asymmetry disrupts stereo matching by affecting the crucial cost volume computation. To address this, we explore the matching cost distribution of two established cost volume construction methods in asymmetric stereo. We find that each cost volume experiences distinct information distortion, indicating that both should be comprehensively utilized to solve the issue. Based on this, we propose the two-phase Iterative Volume Fusion network for Asymmetric Stereo matching (IVF-AStereo). Initially, the aggregated concatenation volume refines the correlation volume. Subsequently, both volumes are fused to enhance fine details. Our method excels in asymmetric scenarios and shows robust performance against significant visual asymmetry. Extensive comparative experiments on benchmark datasets, along with ablation studies, confirm the effectiveness of our approach in asymmetric stereo with resolution and color degradation.

翻译：立体匹配在三维计算机视觉中至关重要，大多数算法假设双目视觉之间具有对称的视觉特性。然而，非对称多相机系统（如长焦-广角相机）的兴起挑战了这一假设，并使立体匹配复杂化。视觉非对称性通过影响关键的成本体积计算来干扰立体匹配。为解决此问题，我们探究了非对称立体中两种现有成本体积构建方法的匹配成本分布。我们发现每种成本体积都经历了不同的信息失真，这表明应全面利用两者来解决该问题。基于此，我们提出了用于非对称立体匹配的两阶段迭代体融合网络（IVF-AStereo）。首先，聚合拼接体积优化相关体积。随后，融合两种体积以增强细节。我们的方法在非对称场景中表现优异，并对显著的视觉非对称性展现出鲁棒性能。在基准数据集上的大量对比实验以及消融研究证实了我们的方法在具有分辨率和色彩退化的非对称立体匹配中的有效性。