HRFuser: A Multi-resolution Sensor Fusion Architecture for 2D Object Detection

Besides standard cameras, autonomous vehicles typically include multipleadditional sensors, such as lidars and radars, which help acquire richerinformation for perceiving the content of the driving scene. While severalrecent works focus on fusing certain pairs of sensors - such as camera andlidar or camera and radar - by using architectural components specific to theexamined setting, a generic and modular sensor fusion architecture is missingfrom the literature. In this work, we focus on 2D object detection, afundamental high-level task which is defined on the 2D image domain, andpropose HRFuser, a multi-resolution sensor fusion architecture that scalesstraightforwardly to an arbitrary number of input modalities. The design ofHRFuser is based on state-of-the-art high-resolution networks for image-onlydense prediction and incorporates a novel multi-window cross-attention block asthe means to perform fusion of multiple modalities at multiple resolutions.Even though cameras alone provide very informative features for 2D detection,we demonstrate via extensive experiments on the nuScenes and Seeing Through Fogdatasets that our model effectively leverages complementary features fromadditional modalities, substantially improving upon camera-only performance andconsistently outperforming state-of-the-art fusion methods for 2D detectionboth in normal and adverse conditions. The source code will be made publiclyavailable.<br

Natural Illumination from Multiple Materials Using Deep Learning

Recovering natural illumination from a single Low-Dynamic Range (LDR) image is a challenging task. To remedy this situation we exploit two properties often found in everyday images. First, images rarely show a single material, but rather multiple ones that all reflect the same illumination. However, the appearance of each material is observed only for some surface orientations, not all. Second, parts of the illumination are often directly observed in the background, without being affected by reflection. Typically, this directly observed part of the illumination is even smaller. We propose a deep Convolutional Neural Network (CNN) that combines prior knowledge about the statistics of illumination and reflectance with an input that makes explicit use of these two observations. Our approach maps multiple partial LDR material observations represented as reflectance maps and a background image to a spherical High-Dynamic Range (HDR) illumination map. For training and testing we propose a new data set comprising of synthetic and real images with multiple materials observed under the same illumination. Qualitative and quantitative evidence shows how both multi-material and using a background are essential to improve illumination estimations

Melatonin treatment in children with therapy-resistant monosymptomatic nocturnal enuresis

Objective: To evaluate the effects of exogenous melatonin on the frequency of wet nights, on the sleep-wake cycle, and on the melatonin profile in children with therapy-resistant MNE. Patients and methods: 24 patients were included. Patients had to maintain a diary including time of sleep and arousal, and whether they had a dry or a wet bed in the morning. We measured baseline melatonin profiles in saliva. Hereafter, patients were randomized to synthetic melatonin or placebo. After 3 and 6 months we evaluated the frequency of enuresis and the melatonin profiles. Results: 11 patients were randomized to melatonin, 13 to placebo. We evaluated melatonin profiles of 7 patients in the melatonin group and of 8 in the placebo group. We observed a change in profile in the melatonin group, but we did not observe a difference in the sleep-wake cycle or the frequency of wet nights in either group. Conclusion: This is the first time exogenous melatonin has been evaluated in the treatment of MNE. Although we observed a change in melatonin profile after the use of exogenous melatonin, we did not observe a change in enuresis frequency or in the sleep-wake cycle of this select group of patients. (C) 2011 Journal of Pediatric Urology Company. Published by Elsevier Ltd. All rights reserved

3D modeling and registration under wide baseline conditions

During the 90s important progess has been made in the area of structure-from-motion. From a series of closely spaced images a 3D model of the observed scene can now be reconstructed, without knowledge about the subsequent camera positions or settings. From nothing but a video, the camera trajectory and scene shape are extracted. Progress has also been important in the area of structured light techniques. Rather than having to use slow and/or bulky laser scanners, compact one-shot systems have been developed. Upon projection of a pattern onto the scene, its 3D shape and texture can be extracted from a single image. This paper presents recent extensions on both strands, that have a common theme: how to cope with large baseline conditions. In the case of shape-from-video we discuss ways to find correspondences and, hence, extract 3D shapes even when the images are taken far apart. In the case of structured light, the problem solved is how to combine partial 3D patches into complete models, without a good initialisation of their relative poses.