REMODE: Probabilistic, Monocular Dense Reconstruction in Real Time

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 state-of-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) and the CUDA-based implementation runs at 30Hz on a laptop computer.


Présenté à:
2014 IEEE International Conference on Robotics and Automation (ICRA 2014), Hong Kong, China, May 31 - June 7, 2014
Année
2014
Laboratoires:




 Notice créée le 2014-06-16, modifiée le 2019-03-16

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