RPNet: an End-to-End Network for Relative Camera Pose Estimation

This paper addresses the task of relative camera pose estimation from raw image pixels, by means of deep neural networks. The proposed RPNet network takes pairs of images as input and directly infers the relative poses, without the need of camera intrinsic/extrinsic. While state-of-the-art systems based on SIFT + RANSAC, are able to recover the translation vector only up to scale, RPNet is trained to produce the full translation vector, in an end-to-end way. Experimental results on the Cambridge Landmark dataset show very promising results regarding the recovery of the full translation vector. They also show that RPNet produces more accurate and more stable results than traditional approaches, especially for hard images (repetitive textures, textureless images, etc). To the best of our knowledge, RPNet is the first attempt to recover full translation vectors in relative pose estimation

Synthetic use of the primary kinetic isotope effect in hydrogen atom transfer: generation of α-aminoalkyl radicals.

addresses: School of Biosciences, University of Exeter, Geoffrey Pope Building, Stocker Road, Exeter, UKEX4 4QD. [email protected]: Journal Article; Research Support, Non-U.S. Gov'tCopyright © 2010 Royal Society of ChemistryThe extent to which deuterium can act as a protecting group to prevent unwanted 1,5-hydrogen atom transfer to aryl and vinyl radical intermediates was examined in the context of the generation of α-aminoalkyl radicals in a pyrrolidine ring. Intra- and intermolecular radical trapping following hydrogen atom transfer provides an illustration of the use of the primary kinetic isotope effect in directing the outcome of synthetic C-C bond-forming processes

DELTAS: Depth Estimation by Learning Triangulation And densification of Sparse points

Multi-view stereo (MVS) is the golden mean between the accuracy of active depth sensing and the practicality of monocular depth estimation. Cost volume based approaches employing 3D convolutional neural networks (CNNs) have considerably improved the accuracy of MVS systems. However, this accuracy comes at a high computational cost which impedes practical adoption. Distinct from cost volume approaches, we propose an efficient depth estimation approach by first (a) detecting and evaluating descriptors for interest points, then (b) learning to match and triangulate a small set of interest points, and finally (c) densifying this sparse set of 3D points using CNNs. An end-to-end network efficiently performs all three steps within a deep learning framework and trained with intermediate 2D image and 3D geometric supervision, along with depth supervision. Crucially, our first step complements pose estimation using interest point detection and descriptor learning. We demonstrate state-of-the-art results on depth estimation with lower compute for different scene lengths. Furthermore, our method generalizes to newer environments and the descriptors output by our network compare favorably to strong baselines. Code is available at https://github.com/magicleap/DELTASComment: ECCV 202

Learning and Matching Multi-View Descriptors for Registration of Point Clouds

Critical to the registration of point clouds is the establishment of a set of accurate correspondences between points in 3D space. The correspondence problem is generally addressed by the design of discriminative 3D local descriptors on the one hand, and the development of robust matching strategies on the other hand. In this work, we first propose a multi-view local descriptor, which is learned from the images of multiple views, for the description of 3D keypoints. Then, we develop a robust matching approach, aiming at rejecting outlier matches based on the efficient inference via belief propagation on the defined graphical model. We have demonstrated the boost of our approaches to registration on the public scanning and multi-view stereo datasets. The superior performance has been verified by the intensive comparisons against a variety of descriptors and matching methods

GeoDesc: Learning Local Descriptors by Integrating Geometry Constraints

Learned local descriptors based on Convolutional Neural Networks (CNNs) have achieved significant improvements on patch-based benchmarks, whereas not having demonstrated strong generalization ability on recent benchmarks of image-based 3D reconstruction. In this paper, we mitigate this limitation by proposing a novel local descriptor learning approach that integrates geometry constraints from multi-view reconstructions, which benefits the learning process in terms of data generation, data sampling and loss computation. We refer to the proposed descriptor as GeoDesc, and demonstrate its superior performance on various large-scale benchmarks, and in particular show its great success on challenging reconstruction tasks. Moreover, we provide guidelines towards practical integration of learned descriptors in Structure-from-Motion (SfM) pipelines, showing the good trade-off that GeoDesc delivers to 3D reconstruction tasks between accuracy and efficiency.Comment: Accepted to ECCV'1

Mental health literacy as a function of remoteness of residence: an Australian national study

<p>Abstract</p> <p>Background</p> <p>Although there have been many population studies of mental health literacy, little is known about the mental health literacy of people who reside in rural areas. This study sought to determine the impact of remoteness on public knowledge of depression and schizophrenia.</p> <p>Methods</p> <p>The mental health literacy of residents of major cities, inner regional, and outer-remote (including outer regional, remote, and very remote) regions were compared using data from a 2003–04 Australian national survey of the mental health literacy of 3998 adults. Measures included the perceived helpfulness of a range of professionals, non-professionals and interventions, and the causes, prognosis, and outcomes after treatment for four case vignettes describing depression, depression with suicidal ideation, early schizophrenia and chronic schizophrenia. Participant awareness of Australia's national depression initiative and depression in the media, their symptoms of depression and exposure to the conditions depicted in the vignettes were also compared.</p> <p>Results</p> <p>Mental health literacy was similar across remoteness categories. However, inner regional residents showed superior identification of the disorders depicted in the suicidal ideation and chronic schizophrenia vignettes. They were also more likely to report having heard of Australia's national depression health promotion campaign. Conversely, they were less likely than major city residents to rate the evidence-based treatment of psychotherapy helpful for depression. Both inner regional and outer-remote residents were less likely to rate psychologists as helpful for depression alone. The rural groups were more likely to rate the non-evidence based interventions of drinking and painkillers as helpful for a depression vignette. In addition, outer-remote residents were more likely to identify the evidence based treatment of antipsychotics as harmful for early schizophrenia and less likely to endorse psychiatrists, psychologists, social workers and general practitioners as helpful for the condition.</p> <p>Conclusion</p> <p>Mental health awareness campaigns in rural and remote regions may be most appropriately focused on communicating which interventions are effective for depression and schizophrenia and which mental health and other professionals are trained in the best-practice delivery and management of these. There is also a need to communicate to rural residents that alcohol and pain relievers are not an effective solution for depression.</p

Moisture transport by Atlantic tropical cyclones onto the North American continent

Tropical Cyclones (TCs) are an important source of freshwater for the North American continent. Many studies have tried to estimate this contribution by identifying TC-induced precipitation events, but few have explicitly diagnosed the moisture fluxes across continental boundaries. We design a set of attribution schemes to isolate the column-integrated moisture fluxes that are directly associated with TCs and to quantify the flux onto the North American Continent due to TCs. Averaged over the 2004–2012 hurricane seasons and integrated over the western, southern and eastern coasts of North America, the seven schemes attribute 7 to 18 % (mean 14 %) of total net onshore flux to Atlantic TCs. A reduced contribution of 10 % (range 9 to 11 %) was found for the 1980–2003 period, though only two schemes could be applied to this earlier period. Over the whole 1980–2012 period, a further 8 % (range 6 to 9 % from two schemes) was attributed to East Pacific TCs, resulting in a total TC contribution of 19 % (range 17 to 22 %) to the ocean-to-land moisture transport onto the North American continent between May and November. Analysis of the attribution uncertainties suggests that incorporating details of individual TC size and shape adds limited value to a fixed radius approach and TC positional errors in the ERA-Interim reanalysis do not affect the results significantly, but biases in peak wind speeds and TC sizes may lead to underestimates of moisture transport. The interannual variability does not appear to be strongly related to the El Nino-Southern Oscillation phenomenon

The role of melatonin in the pathogenesis of adolescent idiopathic scoliosis (AIS)

The cause of adolescent idiopathic scoliosis (AIS) in humans remains obscure and probably multifactorial. At present, there is no proven method or test available to identify children or adolescent at risk of developing AIS or identify which of the affected individuals are at risk of progression. Reported associations are linked in pathogenesis rather than etiologic factors. Melatonin may play a role in the pathogenesis of scoliosis (neuroendocrine hypothesis), but at present, the data available cannot clearly show the role of melatonin in producing scoliosis in humans. The data regarding human melatonin levels are mixed at best, and the melatonin deficiency as a causative factor in the etiology of scoliosis cannot be supported. It will be an important issue of future research to investigate the role of melatonin in human biology, the clinical efficacy, and safety of melatonin under different pathological situations. Research is needed to better define the role of all factors in AIS development

Content-Aware Unsupervised Deep Homography Estimation

Homography estimation is a basic image alignment method in many applications. It is usually conducted by extracting and matching sparse feature points, which are error-prone in low-light and low-texture images. On the other hand, previous deep homography approaches use either synthetic images for supervised learning or aerial images for unsupervised learning, both ignoring the importance of handling depth disparities and moving objects in real world applications. To overcome these problems, in this work we propose an unsupervised deep homography method with a new architecture design. In the spirit of the RANSAC procedure in traditional methods, we specifically learn an outlier mask to only select reliable regions for homography estimation. We calculate loss with respect to our learned deep features instead of directly comparing image content as did previously. To achieve the unsupervised training, we also formulate a novel triplet loss customized for our network. We verify our method by conducting comprehensive comparisons on a new dataset that covers a wide range of scenes with varying degrees of difficulties for the task. Experimental results reveal that our method outperforms the state-of-the-art including deep solutions and feature-based solutions.Comment: Accepted by ECCV 2020 (Oral, Top 2%, 3 over 3 Strong Accepts). Jirong Zhang and Chuan Wang are joint first authors, and Shuaicheng Liu is the corresponding autho

Internet-based treatment for older adults with depression and co-morbid cardiovascular disease: protocol for a randomised, double-blind, placebo controlled trial

<p>Abstract</p> <p>Background</p> <p>Depression, cardiovascular disease (CVD) risk factors and cognitive impairment are important causes of disability and poor health outcomes. In combination they lead to an even worse prognosis. Internet or web-based interventions have been shown to deliver efficacious psychological intervention programs for depression on a large scale, yet no published studies have evaluated their impact among patients with co-existing physical conditions. The aims of this randomised controlled trial are to determine the effects of an evidence-based internet intervention program for depression on depressive mood symptoms, cognitive function and treatment adherence in patients at risk of CVD.</p> <p>Methods/Design</p> <p>This study is an internet-based, double-blind, parallel group randomised controlled trial. The trial will compare the effectiveness of online cognitive behavioural therapy with an online attention control placebo. The trial will consist of a 12-week intervention phase with a 40-week follow-up. It will be conducted in urban and rural New South Wales, Australia and will recruit a community-based sample of adults aged 45 to 75 years. Recruitment, intervention, cognitive testing and follow-up data collection will all be internet-based and automated. The primary outcome is a change in severity of depressive symptoms from baseline to three-months. Secondary outcomes are changes in cognitive function and adherence to treatment for CVD from baseline to three, six and 12-months.</p> <p>Discussion</p> <p>Prior studies of depression amongst patients with CVD have targeted those with previous vascular events and major depression. The potential for intervening earlier in these disease states appears to have significant potential and has yet to be tested. Scalable psychological programs using web-based interventions could deliver care to large numbers in a cost effective way if efficacy were proved. This study will determine the effects of a web-based intervention on depressive symptoms and adherence to treatment among patients at risk of CVD. In addition it will also precisely and reliably define the effects of the intervention upon aspects of cognitive function that are likely to be affected early in at risk individuals, using sensitive and responsive measures.</p> <p>Trial registration</p> <p>Australian New Zealand Clinical Trials Registry (ANZCTR): <a href="http://www.anzctr.org.au/ACTRN12610000085077.aspx">ACTRN12610000085077</a></p