DeMoN: Depth and Motion Network for Learning Monocular Stereo

In this paper we formulate structure from motion as a learning problem. We train a convolutional network end-to-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 two-frame structure from motion methods, results are more accurate and more robust. In contrast to the popular depth-from-single-image networks, DeMoN learns the concept of matching and, thus, better generalizes to structures not seen during training.Comment: Camera ready version for CVPR 2017. Supplementary material included. Project page: http://lmb.informatik.uni-freiburg.de/people/ummenhof/depthmotionnet

Near real-time stereo vision system

The apparatus for a near real-time stereo vision system for use with a robotic vehicle is described. The system is comprised of two cameras mounted on three-axis rotation platforms, image-processing boards, a CPU, and specialized stereo vision algorithms. Bandpass-filtered image pyramids are computed, stereo matching is performed by least-squares correlation, and confidence ranges are estimated by means of Bayes' theorem. In particular, Laplacian image pyramids are built and disparity maps are produced from the 60 x 64 level of the pyramids at rates of up to 2 seconds per image pair. The first autonomous cross-country robotic traverses (of up to 100 meters) have been achieved using the stereo vision system of the present invention with all computing done onboard the vehicle. The overall approach disclosed herein provides a unifying paradigm for practical domain-independent stereo ranging

Is the Endangered Species Act Endangering Species?

We develop theory and present a suite of theoretically consistent empirical measures to explore the extent to which market intervention inadvertently alters resource allocation in a sequentialmove principal/agent game. We showcase our approach empirically by exploring the extent to which the U.S. Endangered Species Act has altered land development patterns. We report evidence indicating significant acceleration of development directly after each of several events deemed likely to raise fears among owners of habitat land. Our preferred estimate suggests an overall acceleration of land development by roughly one year. We also find from complementary hedonic regression models that habitat parcels declined in value when the habitat map was published, which is consistent with our estimates of the degree of preemption. These results have clear implications for policymakers, who continue to discuss alternative regulatory frameworks for species preservation. More generally, our modeling strategies can be widely applied -- from any particular economic environment that has a sequential-move nature to the narrower case of the political economy of regulation.