OmniDepth: Dense Depth Estimation for Indoors Spherical Panoramas.

Recent work on depth estimation up to now has only focused on projective images ignoring 360o content which is now increasingly and more easily produced. We show that monocular depth estimation models trained on traditional images produce sub-optimal results on omnidirectional images, showcasing the need for training directly on 360o datasets, which however, are hard to acquire. In this work, we circumvent the challenges associated with acquiring high quality 360o datasets with ground truth depth annotations, by re-using recently released large scale 3D datasets and re-purposing them to 360o via rendering. This dataset, which is considerably larger than similar projective datasets, is publicly offered to the community to enable future research in this direction. We use this dataset to learn in an end-to-end fashion the task of depth estimation from 360o images. We show promising results in our synthesized data as well as in unseen realistic images

Neural Controller for PTZ cameras based on nonpanoramic foreground detection

Abstract—In this paper a controller for PTZ cameras based on an unsupervised neural network model is presented. It takes advantage of the foreground mask generated by a nonparametric foreground detection subsystem. Thus, our aim is to optimize the movements of the PTZ camera to attain the maximum coverage of the observed scene in presence of moving objects. A growing neural gas (GNG) is applied to enhance the representation of the foreground objects. Both qualitative and quantitative results are reported using several widely used datasets, which demonstrate the suitability of our approach.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Automatic Extrinsic Calibration of Vision and Lidar by Maximizing Mutual Information

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/112212/1/rob21542.pd