Real-time single image depth perception in the wild with handheld devices

Depth perception is paramount to tackle real-world problems, ranging from autonomous driving to consumer applications. For the latter, depth estimation from a single image represents the most versatile solution, since a standard camera is available on almost any handheld device. Nonetheless, two main issues limit its practical deployment: i) the low reliability when deployed in-the-wild and ii) the demanding resource requirements to achieve real-time performance, often not compatible with such devices. Therefore, in this paper, we deeply investigate these issues showing how they are both addressable adopting appropriate network design and training strategies -- also outlining how to map the resulting networks on handheld devices to achieve real-time performance. Our thorough evaluation highlights the ability of such fast networks to generalize well to new environments, a crucial feature required to tackle the extremely varied contexts faced in real applications. Indeed, to further support this evidence, we report experimental results concerning real-time depth-aware augmented reality and image blurring with smartphones in-the-wild.Comment: 11 pages, 9 figure

Self-adapting confidence estimation for stereo

Estimating the confidence of disparity maps inferred by a stereo algorithm has become a very relevant task in the years, due to the increasing number of applications leveraging such cue. Although self-supervised learning has recently spread across many computer vision tasks, it has been barely considered in the eld of confidence estimation. In this paper, we propose a flexible and lightweight solution enabling self-adapting confidence estimation agnostic to the stereo algorithm or network. Our approach relies on the minimum information available in any stereo setup (i.e., the input stereo pair and the output disparity map) to learn an effective confidence measure. This strategy allows us not only a seamless integration with any stereo system, including consumer and industrial devices equipped with undisclosed stereo perception methods, but also, due to its self-adapting capability, for its out-of-the-box deployment in the wild. Exhaustive experimental results with didifferent standard datasets support our claims, showing how our solution is the first-ever enabling online learning of accurate confidence estimation for any stereo system and without any requirement for the end-user

Real-Time Single Image Depth Perception in the Wild with Handheld Devices

Depth perception is paramount for tackling real-world problems, ranging from autonomous driving to consumer applications. For the latter, depth estimation from a single image would represent the most versatile solution since a standard camera is available on almost any handheld device. Nonetheless, two main issues limit the practical deployment of monocular depth estimation methods on such devices: (i) the low reliability when deployed in the wild and (ii) the resources needed to achieve real-time performance, often not compatible with low-power embedded systems. Therefore, in this paper, we deeply investigate all these issues, showing how they are both addressable by adopting appropriate network design and training strategies. Moreover, we also outline how to map the resulting networks on handheld devices to achieve real-time performance. Our thorough evaluation highlights the ability of such fast networks to generalize well to new environments, a crucial feature required to tackle the extremely varied contexts faced in real applications. Indeed, to further support this evidence, we report experimental results concerning real-time, depth-aware augmented reality and image blurring with smartphones in the wild

Protocolli gestionali-diagnostico-terapeutico-assistenziali in chirurgica tiroidea. 2° Consensus Conference.

Aim. To review and to update the management protocols in thyroid surgery proposed two years ago by 1st Consensus Conference called on the topic by the Italian Association of Endocrine Surgery Units (UEC Club). Method. The 2nd Consensus Conference took place November 30, 2008 in Pisa within the framework of the 7th National Congress of the UEC Club. A selected board of endocrinologists and endocrine surgeons (chairmans: Paolo Miccoli and Aldo Pinchera; speaker: Lodovico Rosato) examined the individual chapters and submitted the consensus text for the approval of several experts. This plain and concise text provides the rationale of the thyroid patient management and wants to be the most complete possible tool for the physicians and other professionals in the field. Conclusions. The diagnostic, therapeutic and healthcare management protocols in thyroid surgery approved by the 2nd Consensus Conference are officially those proposed by the Italian Association of Endocrine Surgery Units (UEC Club) and are subject to review by two years