Context-Guided Multi-view Stereo with Depth Back-Projection

Tianxing Feng, Zhe Zhang, Kaiqiang Xiong, Ronggang Wang

Abstract: Depth map based Multi-view stereo (MVS) is a task that focuses on taking images from multiple views of one same scene as input, estimating depth in each view, and generating 3D reconstructions of objects in the scene. Though most matching based MVS methods take features of the input images into account, few of them make the best of the underlying global information in images. They may suffer from difficult image regions, such as object boundaries, low-texture areas, and reflective surfaces. Human beings perceive these cases with the help of global awareness, that is to say, the context of the objects we observe. Similarly, we propose Context-guided Multi-view Stereo (ContextMVS), a coarse-to-fine pyramidal MVS network, which explicitly utilizes the context guidance in asymmetrical features to integrate global information into the 3D cost volume for feature matching. Also, with a low computational overhead, we adopt a depth back-projection refined up-sampling module to improve the non-parametric depth up-sampling between pyramid levels. Experimental results indicate that our method outperforms classical learning-based methods by a large margin on public benchmarks, DTU and Tanks and Temples, demonstrating the effectiveness of our method.