In this thesis, we explore methods of building dense depth map from monocular video. First, we introduce our multi-view stereo pipeline, which utilizes photometric bundle adjustment for getting accurate depth of textured regions from small motion video. Second, we improve the depth estimation of low-texture region by fusing deep convolutional network predictions.

We categorize the depth fusion methods into two categories: late integration and early integration. Late integration uses highly confident partial depth from pure geometric methods as anchor points to refine the dense depth map generated by deep convolutional networks. However in this case, the network output is not guaranteed to be aligned with confident partial depth, thus the fusion process might be problematic. To improve this issue, we propose early integration, which uses confident partial depths as constraints for deep convolutional networks. This method ensures the two depth sources to be well-aligned and thus has better depth accuracy than previous methods.