  Multi-view stereo is an important research task in computer vision while
still keeping challenging. In recent years, deep learning-based methods have
shown superior performance on this task. Cost volume pyramid network-based
methods which progressively refine depth map in coarse-to-fine manner, have
yielded promising results while consuming less memory. However, these methods
fail to take fully consideration of the characteristics of the cost volumes in
each stage, leading to adopt similar range search strategies for each cost
volume stage. In this work, we present a novel cost volume pyramid based
network with different searching strategies for multi-view stereo. By choosing
different depth range sampling strategies and applying adaptive unimodal
filtering, we are able to obtain more accurate depth estimation in low
resolution stages and iteratively upsample depth map to arbitrary resolution.
We conducted extensive experiments on both DTU and BlendedMVS datasets, and
results show that our method outperforms most state-of-the-art methods.
