The performance of PatchMatch-based multi-view stereo algorithms depends heavily on the source views selected for computing matching costs. Instead of modeling the visibility of different views, most existing approaches handle occlusions in an ad-hoc manner. To address this issue, we propose a novel visibility-guided pixelwise view selection scheme in this paper. It progressively refines the set of source views to be used for each pixel in the reference view based on visibility information provided by already validated solutions. In addition, the Artificial Multi-Bee Colony (AMBC) algorithm is employed to search for optimal solutions for different pixels in parallel. Inter-colony communication is performed both within the same image and among different images. Fitness rewards are added to validated and propagated solutions, effectively enforcing the smoothness of neighboring pixels and allowing better handling of textureless areas. Experimental results on the DTU dataset show our method achieves state-of-the-art performance among non-learning-based methods and retrieves more details in occluded and low-textured regions.

翻译：基于PatchMatch的多视角立体算法性能高度依赖于用于计算匹配代价的源视图选择。现有方法大多采用临时性策略处理遮挡问题，而未能对多视图的可视性进行建模。针对该问题，本文提出一种新颖的可见性引导逐像素视图选择方案。该方案基于已验证解提供的可见性信息，渐进式优化参考图像中每个像素所关联的源视图集合。此外，采用人工多蜂群算法并行搜索不同像素的最优解，通过图像内部及跨图像通信实现蜂群协同交互。通过向已验证和传播的解添加适应度奖励，有效强化相邻像素的平滑性约束，并提升对无纹理区域的鲁棒性。在DTU数据集上的实验表明，本方法在非学习方法中达到了最优性能，能够从遮挡和低纹理区域恢复更多细节。