Publication
We report on the use of deep learning algorithms to perform depth recovery in multiview imaging. We show that if enough training data are provided, a neural network such as multilayer perceptron can be trained to recover the depth in multiview imaging as a regression problem. Such a method can replace camera calibration since no knowledge on the camera configuration is required during training. Another advantage of deep learning for this problem, is the speed of testing; typically a few microseconds per point in the scene. This is a lot better than state-of-art algorithms that require to solve a full optimization problem. In a second part, we have studied a related problem: detecting changes in the camera setting. We have shown that deep learning classifiers can recognize amongst a few (4 or 5) camera settings based only on the projections of points on the cameras, with less than 1% classification error. This is a promising step towards the SLAM problem.
The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is tr ...
Francesco Mondada, Alexandre Massoud Alahi, Vaios Papaspyros