Recent work on depth estimation up to now has only focused on projective images ignoring \({360}^{\circ }\) 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 \({360}^{\circ }\) datasets, which however, are hard to acquire. In this work, we circumvent the challenges associated with acquiring high quality \({360}^{\circ }\) datasets with ground truth depth annotations, by re-using recently released large scale 3D datasets and re-purposing them to \({360}^{\circ }\) 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 \({360}^{\circ }\) images. We show promising results in our synthesized data as well as in unseen realistic images.

Since December 2019, the world has been devastated by the Coronavirus Disease 2019 (COVID-19) pandemic. Emergency Departments have been experiencing situations of urgency where clinical experts, without long experience and mature means in the fight against COVID-19, have to rapidly decide the most proper patient treatment. In this context, we introduce an artificially intelligent tool for effective and efficient Computed Tomography (CT)-based risk assessment to improve treatment and patient care. In this paper, we introduce a data-driven approach built on top of volume-of-interest aware deep neural networks for automatic COVID-19 patient risk assessment (discharged, hospitalized, intensive care unit) based on lung infection quantization through segmentation and, subsequently, CT classification. We tackle the high and varying dimensionality of the CT input by detecting and analyzing only a sub-volume of the CT, the Volume-of-Interest (VoI). Differently from recent strategies that consider infected CT slices without requiring any spatial coherency between them, or use the whole lung volume by applying abrupt and lossy volume down-sampling, we assess only the “most infected volume” composed of slices at its original spatial resolution. To achieve the above, we create, present and publish a new labeled and annotated CT dataset with 626 CT samples from COVID-19 patients. The comparison against such strategies proves the effectiveness of our VoI-based approach. We achieve remarkable performance on patient risk assessment evaluated on balanced data by reaching 88.88%, 89.77%, 94.73% and 88.88% accuracy, sensitivity, specificity and F1-score, respectively.

Multi-view capture systems are complex systems to engineer. They require technical knowledge to install and intricate processes to setup related mainly to the sensors' spatial alignment (i.e. external calibration). However, with the ongoing developments in new production methods, we are now at a position where the production of high quality realistic 3D assets is possible even with commodity sensors. Nonetheless, the capturing systems developed with these methods are heavily intertwined with the methods themselves, relying on custom solutions and seldom - if not at all - publicly available. In light of this, we design, develop and publicly offer a multi-view capture system based on the latest RGB-D sensor technology. For our system, we develop a portable and easy-to-use external calibration method that greatly reduces the effort and knowledge required, as well as simplify the overall process.

In this paper we document our work that extends the nevergrad platform by adding a real-world benchmark at the intersection of computer vision and graphics: 3D Performance Capture. Detailed documentation can be found at vcl3d.github.io/nevergrad/