Dense 3D Scene Reconstruction from Multiple Spherical Images for 3-DoF+ VR Applications

We propose a novel method for estimating the 3D geometry of indoor scenes based on multiple spherical images. Our technique produces a dense depth map registered to a reference view so that depth-image-based-rendering (DIBR) techniques can be explored for providing three-degrees-of-freedom plus immersive experiences to virtual reality users. The core of our method is to explore large displacement optical flow algorithms to obtain point correspondences, and use cross-checking and geometric constraints to detect and remove bad matches. We show that selecting a subset of the best dense matches leads to better pose estimates than traditional approaches based on sparse feature matching, and explore a weighting scheme to obtain the depth maps. Finally, we adapt a fast image-guided filter to the spherical domain for enforcing local spatial consistency, improving the 3D estimates. Experimental results indicate that our method quantitatively outperforms competitive approaches on computer-generated images and synthetic data under noisy correspondences and camera poses. Also, we show that the estimated depth maps obtained from only a few real spherical captures of the scene are capable of producing coherent synthesized binocular stereoscopic views by using traditional DIBR methods.