High-Quality Depth Recovery via Interactive Multi-view Stereo

Although multi-view stereo has been extensively studied during the past decades, automatically computing high-quality dense depth information from captured images/videos is still quite difficult. Many factors, such as serious occlusion, large texture less regions and strong reflection, easily cause erroneous depth recovery. In this paper, we present a novel semi-automatic multi-view stereo system, which can quickly create and repair depth from a monocular sequence taken by a freely moving camera. One of our main contributions is that we propose a novel multi-view stereo model incorporating prior constraints indicated by user interaction, which makes it possible to even handle Non-Lambertian surface that surely violates the photo-consistency constraint. Users only need to provide a coarse segmentation and a few user interactions, our system can automatically correct depth and refine boundary. With other priors and occlusion handling, the erroneous depth can be effectively corrected even for very challenging examples that are difficult for state-of-the-art methods.