解决双目相机视觉不对称的立体匹配问题
Iterative Volume Fusion for Asymmetric Stereo Matching
- 分两阶段迭代融合两个成本体积，提升不对称场景匹配精度
- 在基准数据集上实现优于现有方法的精度，对分辨率与色彩退化鲁棒
- 适合用于远近镜头组合等非对称多摄像头系统
立体匹配在三维计算机视觉中至关重要，传统算法通常假设双目视觉具有对称性。然而，随着远摄-广角等非对称多摄像机系统的发展，这一假设受到挑战，导致立体匹配困难。视觉不对称会破坏关键的成本体积计算。本文研究了两种主流成本体积构建方法在非对称立体匹配中的匹配代价分布，发现每个成本体积均存在不同的信息失真，因此应充分结合两者优势。基于此，提出两阶段迭代体积融合网络（IVF-AStereo）。首先，聚合拼接体积优化相关体积；随后，将两个体积融合以增强细节。实验在多个基准数据集上验证了该方法在显著视觉不对称下的优异性能，且对分辨率和颜色退化具有强鲁棒性。
原文摘要 · Abstract (English)
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.
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