REMODE: Probabilistic, Monocular Dense Reconstruction in Real Time by Matia Pizzoli, Christian Forster and Davide Scaramuzza
The accompanying video and source code are available at: http://rpg.ifi.uzh.ch/softwareAbstract— In this paper, we solve the problem of estimating dense and accurate depth maps from a single moving camera. A probabilistic depth measurement is carried out in real time on a per-pixel basis and the computed uncertainty is used to reject erroneous estimations and provide live feedback on the reconstruction progress. Our contribution is a novel approach to depth map computation that combines Bayesian estimation and recent development on convex optimization for image processing. We demonstrate that our method outperforms statof-the-art techniques in terms of accuracy, while exhibiting high efﬁciency in memory usage and computing power. We call our approach REMODE (REgularized MOnocular Depth Estimation). Our CUDA-based implementation runs at 30Hz on a laptop computer and is released as open-source software.
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