沈阳理工大学自动化与电气工程学院;
多视图立体匹配（Multi-View Stereo）是计算机视觉领域的重要任务之一，旨在从多个视角的图像中恢复场景的结构信息。然而，由于成本体积聚合在局部存在着严重的不一致性，直接聚合几何相邻成本会导致严重错误导向。现有的方法要么寻求二维空间的最优选择性聚集，要么增加聚集的手段，但都无法有效解决成本体积的几何不一致性，导致深度估计的精度和鲁棒性不佳。为了解决这个问题，提出用于多视图立体的协同表达（CRMVS），旨在协同多个模块整合几何的一致性信息，提高多视图立体匹配任务的深度估计精度和鲁棒性。首先，利用改进的特征金字塔网络（FPN）增强网络的特征提取能力。其次，设计了一个渐进式权重网络模块（PWN）进行代价体的构建。最后，设计了一个几何代价聚合与精化网络模块（GCR）来对代价体进行精准聚合。实验结果表明在DTU,Tanks&Temple数据集上都展现出了先进的性能。
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基本信息:
DOI：
中图分类号:TP391.41
引用信息:
[1]朱治年,刘韵婷,肖培宇等.多阶段协同的多视图立体算法研究[J].通信与信息技术,2025,No.274(02):28-32.
基金信息: