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 的多视图立体算法的性能在很大程度上取决于为计算匹配成本而选择的源视图。 大多数现有方法不是模拟不同观点的可见度,而是以临时方式处理隔离问题。 为了解决这个问题,我们提议在本文件中采用新的可见度引导像素视图选择方案。它根据已经验证的解决方案提供的可见度信息,逐步完善参考视图中用于每个像素的源视图集。此外,还采用人工多 Bee Colony(AMBC) 算法,为平行的不同像素寻找最佳解决方案。 跨殖民地通信在相同图像中和不同图像中进行。 适应性奖励被添加到验证和宣传的解决方案中,有效增强相邻像素的光滑度,并允许更好地处理无纹区。 DTU 数据集的实验结果显示,我们的方法在非学习方法中达到了最先进的性能,并在隐蔽和低文本区域检索更多细节。