Single-View and Multiview Depth Fusion

Abstract

Dense and accurate 3-D mapping from a monocular sequence is a key technology for several applications and still an open research area. This letter leverages recent results on single-view convolutional network (CNN)-based depth estimation and fuses them with multiview depth estimation. Both approaches present complementary strengths. Multiview depth is highly accurate but only in high-texture areas and high-parallax cases. Single-view depth captures the local structure of midlevel regions, including texture-less areas, but the estimated depth lacks global coherence. The single and multiview fusion we propose is challenging in several aspects. First, both depths are related by a deformation that depends on the image content. Second, the selection of multiview points of high accuracy might be difficult for low-parallax configurations. We present contributions for both problems. Our results in the public datasets of NYUv2 and TUM shows that our algorithm outperforms the individual single and multiview approaches. A video showing the key aspects of mapping in our single and multiview depth proposal is available at https://youtu.be/ipc5HukTb4k.