Cityscapes 3D: Dataset and Benchmark for 9 DoF Vehicle Detection

Detecting vehicles and representing their position and orientation in the three dimensional space is a key technology for autonomous driving. Recently, methods for 3D vehicle detection solely based on monocular RGB images gained popularity. In order to facilitate this task as well as to compare and drive state-of-the-art methods, several new datasets and benchmarks have been published. Ground truth annotations of vehicles are usually obtained using lidar point clouds, which often induces errors due to imperfect calibration or synchronization between both sensors. To this end, we propose Cityscapes 3D, extending the original Cityscapes dataset with 3D bounding box annotations for all types of vehicles. In contrast to existing datasets, our 3D annotations were labeled using stereo RGB images only and capture all nine degrees of freedom. This leads to a pixel-accurate reprojection in the RGB image and a higher range of annotations compared to lidar-based approaches. In order to ease multitask learning, we provide a pairing of 2D instance segments with 3D bounding boxes. In addition, we complement the Cityscapes benchmark suite with 3D vehicle detection based on the new annotations as well as metrics presented in this work. Dataset and benchmark are available online.Comment: 2020 "Scalability in Autonomous Driving" CVPR Worksho

Cityscapes 3D: Dataset and Benchmark for 9 DoF Vehicle Detection

Detecting vehicles and representing their position and orientation in the three dimensional space is a key technology for autonomous driving. Recently, methods for 3D vehicle detection solely based on monocular RGB images gained popularity. In order to facilitate this task as well as to compare and drive state-of-the-art methods, several new datasets and benchmarks have been published. Ground truth annotations of vehicles are usually obtained using lidar point clouds, which often induces errors due to imperfect calibration or synchronization between both sensors. To this end, we propose Cityscapes 3D, extending the original Cityscapes dataset with 3D bounding box annotations for all types of vehicles. In contrast to existing datasets, our 3D annotations were labeled using stereo RGB images only and capture all nine degrees of freedom. This leads to a pixel-accurate reprojection in the RGB image and a higher range of annotations compared to lidar-based approaches. In order to ease multitask learning, we provide a pairing of 2D instance segments with 3D bounding boxes. In addition, we complement the Cityscapes benchmark suite with 3D vehicle detection based on the new annotations as well as metrics presented in this work. Dataset and benchmark are available online

Isotopic Fractionation of Zn During Impact on Earth

International audienceThe isotope fractionation of Zn in lunar soils [1] is surprisingly large (up to 3 per mil per amu). These fractionations have been attributed to vaporization due to impact by micrometeorites onto the surface of the moon. Recently, Albarede et al. [2] extended the study of isotopic fractionation of Zn to shocked rocks from a terrestrial impact site, Meteor Crater. They observed a negative correlation between isotope compositions and shock grade in 7 samples of Coconino sandstone and concluded that, even on Earth, vaporization at high temperature due to impact is capable to fractionate the isotopic compositions of rather heavy elements to a measurable extent. Here, we investigate the degree of isotopic fractionation of Zn in rocks from two terrestrial impact craters: (1) impactites from the Lonar crater, India, and (2) the impact melt rocks from the Bosumtwi crater (Ghana), as well as the related Ivory Coast tektites (IVC

Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometrically Constrained Keypoints in Real-Time

In this paper we propose a novel 3D single-shot object detection method for detecting vehicles in monocular RGB images. Our approach lifts 2D detections to 3D space by predicting additional regression and classification parameters and hence keeping the runtime close to pure 2D object detection. The additional parameters are transformed to 3D bounding box keypoints within the network under geometric constraints. Our proposed method features a full 3D description including all three angles of rotation without supervision by any labeled ground truth data for the object's orientation, as it focuses on certain keypoints within the image plane. While our approach can be combined with any modern object detection framework with only little computational overhead, we exemplify the extension of SSD for the prediction of 3D bounding boxes. We test our approach on different datasets for autonomous driving and evaluate it using the challenging KITTI 3D Object Detection as well as the novel nuScenes Object Detection benchmarks. While we achieve competitive results on both benchmarks we outperform current state-of-the-art methods in terms of speed with more than 20 FPS for all tested datasets and image resolutions

A health promotion focus on COVID-19: Keep the Trojan horse out of our health systems. Promote health for ALL in times of crisis and beyond!

PREFACE Ongoing discussion about the range of actions needed during the SARS-CoV-2 outbreak and Coronavirus Disease 2019 (COVID-19) is calling all of us to bring forward our thoughts and experiences about how best to cope with the multiple challenges we are facing regarding COVID-19. We would like to share the following five talking points that could evolve into a more refined and targeted public health discussion on the implications of this pandemic, from a health promotion perspective. As discussions worldwide bring topics such as health, equity, sustainability, solidarity or human dignity to a new level of implications, a systematic perspective is missing to bring these themes together with the disease prevention and curative efforts in the public health framework. This is where health promotion has the expertise to bring these extremely relevant issues together, to offer a comprehensive approach, in a common effort to support the medical care systems to face the sudden burden that was laid in their hands

The Cardiovascular Comorbidity in Children with Chronic Kidney Disease (4C) Study: Objectives, Design, and Methodology

Background and objectives: Children and adolescents with chronic kidney disease (CKD) are at high risk for cardiovascular morbidity and mortality. A systemic arteriopathy and cardiomyopathy has been characterized in pediatric dialysis patients by the presence of morphologic and functional abnormalities