3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration

In this paper, we propose the 3DFeat-Net which learns both 3D feature detector and descriptor for point cloud matching using weak supervision. Unlike many existing works, we do not require manual annotation of matching point clusters. Instead, we leverage on alignment and attention mechanisms to learn feature correspondences from GPS/INS tagged 3D point clouds without explicitly specifying them. We create training and benchmark outdoor Lidar datasets, and experiments show that 3DFeat-Net obtains state-of-the-art performance on these gravity-aligned datasets.Comment: 17 pages, 6 figures. Accepted in ECCV 201

Unsupervised Monocular Depth Estimation for Night-time Images using Adversarial Domain Feature Adaptation

In this paper, we look into the problem of estimating per-pixel depth maps from unconstrained RGB monocular night-time images which is a difficult task that has not been addressed adequately in the literature. The state-of-the-art day-time depth estimation methods fail miserably when tested with night-time images due to a large domain shift between them. The usual photo metric losses used for training these networks may not work for night-time images due to the absence of uniform lighting which is commonly present in day-time images, making it a difficult problem to solve. We propose to solve this problem by posing it as a domain adaptation problem where a network trained with day-time images is adapted to work for night-time images. Specifically, an encoder is trained to generate features from night-time images that are indistinguishable from those obtained from day-time images by using a PatchGAN-based adversarial discriminative learning method. Unlike the existing methods that directly adapt depth prediction (network output), we propose to adapt feature maps obtained from the encoder network so that a pre-trained day-time depth decoder can be directly used for predicting depth from these adapted features. Hence, the resulting method is termed as "Adversarial Domain Feature Adaptation (ADFA)" and its efficacy is demonstrated through experimentation on the challenging Oxford night driving dataset. Also, The modular encoder-decoder architecture for the proposed ADFA method allows us to use the encoder module as a feature extractor which can be used in many other applications. One such application is demonstrated where the features obtained from our adapted encoder network are shown to outperform other state-of-the-art methods in a visual place recognition problem, thereby, further establishing the usefulness and effectiveness of the proposed approach.Comment: ECCV 202

The effect of communication between the right and left liver on the outcome of surgical drainage for jaundice due to malignant obstruction at the hilus of the liver

Debate continues regarding the optimal management of irresectable malignant proximal biliary obstruction. Controversy exists concerning the ability of unilateral drainage to provide adequate biliary decompression with tumors that have occluded the communication between the right and left hepatic ductal systems. Between October 1986 and October 1989, 18 patients with malignant proximal biliary obstruction were treated by an intrahepatic biliary enteric bypass. Patients were divided into two groups based on the presence or absence of a communication between the right and left biliary systems. In Group I (n = 9), there was free communication; and in Group II (n = 9) there was no communication. There were two perioperative deaths (11%) one due to persistent cholangitis and the other to myocardial insufficiency both with one death in each group. The median survival (excluding perioperative deaths) was 5.6 months. Comparison of pre- and postoperative serum levels of bilirubin and alkaline phosphatase showed a significant decrease in each group, but no difference between the groups in the size of the reduction. Sixteen patients survived at least three months and the palliation was judged as excellent in eight, fair in five, and unchanged in three. These results demonstrate the effectiveness of biliary enteric bypass regardless of communication between the left and right biliary ductal systems.H. U. Baer, M. Rhyner, S. C. Stain, P. W. Glauser, A. R. Dennison, G. J. Maddern, and L. H. Blumgar

VPR-Bench: An Open-Source Visual Place Recognition Evaluation Framework with Quantifiable Viewpoint and Appearance Change

Visual place recognition (VPR) is the process of recognising a previously visited place using visual information, often under varying appearance conditions and viewpoint changes and with computational constraints. VPR is related to the concepts of localisation, loop closure, image retrieval and is a critical component of many autonomous navigation systems ranging from autonomous vehicles to drones and computer vision systems. While the concept of place recognition has been around for many years, VPR research has grown rapidly as a field over the past decade due to improving camera hardware and its potential for deep learning-based techniques, and has become a widely studied topic in both the computer vision and robotics communities. This growth however has led to fragmentation and a lack of standardisation in the field, especially concerning performance evaluation. Moreover, the notion of viewpoint and illumination invariance of VPR techniques has largely been assessed qualitatively and hence ambiguously in the past. In this paper, we address these gaps through a new comprehensive open-source framework for assessing the performance of VPR techniques, dubbed “VPR-Bench”. VPR-Bench (Open-sourced at: https://github.com/MubarizZaffar/VPR-Bench) introduces two much-needed capabilities for VPR researchers: firstly, it contains a benchmark of 12 fully-integrated datasets and 10 VPR techniques, and secondly, it integrates a comprehensive variation-quantified dataset for quantifying viewpoint and illumination invariance. We apply and analyse popular evaluation metrics for VPR from both the computer vision and robotics communities, and discuss how these different metrics complement and/or replace each other, depending upon the underlying applications and system requirements. Our analysis reveals that no universal SOTA VPR technique exists, since: (a) state-of-the-art (SOTA) performance is achieved by 8 out of the 10 techniques on at least one dataset, (b) SOTA technique in one community does not necessarily yield SOTA performance in the other given the differences in datasets and metrics. Furthermore, we identify key open challenges since: (c) all 10 techniques suffer greatly in perceptually-aliased and less-structured environments, (d) all techniques suffer from viewpoint variance where lateral change has less effect than 3D change, and (e) directional illumination change has more adverse effects on matching confidence than uniform illumination change. We also present detailed meta-analyses regarding the roles of varying ground-truths, platforms, application requirements and technique parameters. Finally, VPR-Bench provides a unified implementation to deploy these VPR techniques, metrics and datasets, and is extensible through templates

The landscape of gifted and talented education in England and Wales: How are teachers implementing policy?

This is an Author's Accepted Manuscript of an article published in Research Papers in Education, 27(2), 167-186, 2012, copyright Taylor & Francis, available online at: http://www.tandfonline.com/10.1080/02671522.2010.509514.This paper explores the evidence relating to how primary schools are responding to the ‘gifted and talented’ initiative in England and Wales. A questionnaire survey which invited both closed and open-ended responses was carried out with a national sample of primary schools. The survey indicated an increasing proportion of coordinators, compared with a survey carried out in 1996, were identifying their gifted and talented children as well as having associated school policies. However, the survey also highlighted a number of issues which need addressing if the initiative is to achieve its objective of providing the best possible educational opportunities for children. For example, it was found that a significant number of practitioners were not aware of the existence of the National Quality Standards for gifted and talented education, provided by the UK government in 2007, and the subject-specific criteria provided by the UK’s Curriculum Authority for identification and provision have been largely ignored. The process of identifying children to be placed on the ‘gifted and talented’ register seems haphazard and based on pragmatic reasons. Analysis of teachers’ responses also revealed a range of views and theoretical positioning held by them, which have implications for classroom practice. As the ‘gifted and talented’ initiative in the UK is entering a second decade, and yet more significant changes in policy are introduced, pertinent questions need to be raised and given consideration

Norbornane: An investigation into its valence electronic structure using electron momentum spectroscopy, and density functional and Green's function theories

CIRCULATION & FULFILLMENT DIV, 2 HUNTINGTON QUADRANGLE, STE 1 N O 1, MELVILLE, USA, NY, 11747-450

Ethical and regulatory implications of the COVID-19 pandemic for the medical devices industry and its representatives

The development and deployment of medical devices, along with most areas of healthcare, has been signifcantly impacted by the COVID-19 pandemic. This has had variable ethical implications, two of which we will focus on here. First, medical device regulations have been rapidly amended to expedite approvals of devices ranging from face masks to ventilators. Although some regulators have issued cessation dates, there is inadequate discussion of trig‑ gers for exiting these crisis standards, and evidence that this may not be feasible. Given the relatively low evidence standards currently required for regulatory approval of devices, this further indefnite reduction in standards raises serious ethical issues. Second, the pandemic has disrupted the usual operations of device representatives in hospi‑ tals, providing an opportunity to examine and refne this potentially ethically problematic practice. In this paper we explain and critically analyse the ethical implications of these two pandemic-related impacts on medical devices and propose suggestions for their management. These include an endpoint for pandemic-related adjustments to device regulation or a mechanism for continued refnement over time, together with a review of device research conducted under crisis conditions, support for the removal and replacement of emergency approved devices, and a review of device representative credentialling.Brette Blakely, Wendy Rogers, Jane Johnson, Quinn Grundy, Katrina Hutchison, Robyn Clay, Williams, Bernadette Richards, and Guy Madder

Beyond Controlled Environments: 3D Camera Re-Localization in Changing Indoor Scenes

Long-term camera re-localization is an important task with numerous computer vision and robotics applications. Whilst various outdoor benchmarks exist that target lighting, weather and seasonal changes, far less attention has been paid to appearance changes that occur indoors. This has led to a mismatch between popular indoor benchmarks, which focus on static scenes, and indoor environments that are of interest for many real-world applications. In this paper, we adapt 3RScan - a recently introduced indoor RGB-D dataset designed for object instance re-localization - to create RIO10, a new long-term camera re-localization benchmark focused on indoor scenes. We propose new metrics for evaluating camera re-localization and explore how state-of-the-art camera re-localizers perform according to these metrics. We also examine in detail how different types of scene change affect the performance of different methods, based on novel ways of detecting such changes in a given RGB-D frame. Our results clearly show that long-term indoor re-localization is an unsolved problem. Our benchmark and tools are publicly available at waldjohannau.github.io/RIO10Comment: ECCV 2020, project website https://waldjohannau.github.io/RIO1

Single-Image Depth Prediction Makes Feature Matching Easier

Good local features improve the robustness of many 3D re-localization and multi-view reconstruction pipelines. The problem is that viewing angle and distance severely impact the recognizability of a local feature. Attempts to improve appearance invariance by choosing better local feature points or by leveraging outside information, have come with pre-requisites that made some of them impractical. In this paper, we propose a surprisingly effective enhancement to local feature extraction, which improves matching. We show that CNN-based depths inferred from single RGB images are quite helpful, despite their flaws. They allow us to pre-warp images and rectify perspective distortions, to significantly enhance SIFT and BRISK features, enabling more good matches, even when cameras are looking at the same scene but in opposite directions.Comment: 14 pages, 7 figures, accepted for publication at the European conference on computer vision (ECCV) 202