Deep Single and Direct Multi-View Depth Fusion

Dense 3D mapping from a monocular sequence is a key technology for several applications and still a research problem. This paper leverages recent results on single-view CNN-based depth estimation and fuses them with direct multi-view depth estimation. Both approaches present complementary strengths. Multi-view depth estimation is highly accurate but only in high-texture and high-parallax cases. Single-view depth captures the local structure of mid-level regions, including textureless areas, but the estimated depth lacks global coherence. The single and multi-view fusion we propose has several challenges. First, both depths are related by a non-rigid deformation that depends on the image content. And second, the selection of multi-view points of high accuracy might be difficult for low-parallax configurations. We present contributions for both problems. Our results in the public datasets of NYU and TUM shows that our algorithm outperforms the individual single and multi-view approaches.