360MVSNet: Deep Multi-view Stereo Network with 360° Images for Indoor Scene Reconstruction

Recent multi-view stereo methods have achieved promising results with the advancement of deep learning techniques. Despite of the progress, due to the limited fields of view of regular images, reconstructing large indoor environments still requires collecting many images with sufficient visual overlap, which is quite labor-intensive. 360 degrees images cover a much larger field of view than regular images and would facilitate the capture process. In this paper, we present 360MVSNet, the first deep learning network for multi-view stereo with 360 degrees images. Our method combines uncertainty estimation with a spherical sweeping module for 360 degrees images captured from multiple viewpoints in order to construct multi-scale cost volumes. By regressing volumes in a coarse-to-fine manner, high-resolution depth maps can be obtained. Furthermore, we have constructed EQMVS, a large-scale synthetic dataset that consists of over 50K pairs of RGB and depth maps in equirectangular projection. Experimental results demonstrate that our method can reconstruct large synthetic and real-world indoor scenes with significantly better completeness than previous traditional and learning-based methods while saving both time and effort in the data acquisition process.