In this paper we formulate structure from motion as a learning problem. We train a convolutional network endto-end to compute depth and camera motion from successive, unconstrained image pairs. The architecture is composed of multiple stacked encoder-decoder networks, the core part being an iterative network that is able to improve its own predictions. The network estimates not only depth and motion, but additionally surface normals, optical flow between the images and confidence of the matching. A crucial component of the approach is a training loss based on spatial relative differences. Compared to traditional twoframe structure from motion methods, results are more accurate and more robust. In contrast to the popular depthfrom-single-image networks, DeMoN learns the concept of matching and, thus, better generalizes to structures not seen during training.
Ummenhofer et al. REF trained a convolutional network end-to-end to compute depth and camera motion from successive, unconstrained image pairs, where the architecture is composed of multiple stacked encoder-decoder networks.
DeMoN: Depth and Motion Network for Learning Monocular Stereo
ArXiv
ArXiv
Computer Science