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Dense and accurate 3D mapping from a monocular sequence is a key technologyfor several applications and still an open research area. This paper leveragesrecent results on single-view CNN-based depth estimation and fuses them withmulti-view depth estimation. Both approaches present complementary strengths.Multi-view depth is highly accurate but only in high-texture areas andhigh-parallax cases. Single-view depth captures the local structure ofmid-level regions, including texture-less areas, but the estimated depth lacksglobal coherence. The single and multi-view fusion we propose is challenging inseveral aspects. First, both depths are related by a deformation that dependson the image content. Second, the selection of multi-view points of highaccuracy might be difficult for low-parallax configurations. We presentcontributions for both problems. Our results in the public datasets of NYUv2and TUM shows that our algorithm outperforms the individual single andmulti-view approaches. A video showing the key aspects of mapping in our Singleand Multi-view depth proposal is available at https://youtu.be/ipc5HukTb4k

Single-View and Multi-View Depth Fusion