SliceMatch: Geometry-guided Aggregation for Cross-View Pose Estimation

This work addresses cross-view camera pose estimation, i.e., determining the 3-Degrees-of-Freedom camera pose of a given ground-level image w.r.t. an aerial image of the local area. We propose SliceMatch, which consists of ground and aerial feature extractors, feature aggregators, and a pose predictor. The feature extractors extract dense features from the ground and aerial images. Given a set of candidate camera poses, the feature aggregators construct a single ground descriptor and a set of pose-dependent aerial descriptors. Notably, our novel aerial feature aggregator has a cross-view attention module for ground-view guided aerial feature selection and utilizes the geometric projection of the ground camera's viewing frustum on the aerial image to pool features. The efficient construction of aerial descriptors is achieved using precomputed masks. SliceMatch is trained using contrastive learning and pose estimation is formulated as a similarity comparison between the ground descriptor and the aerial descriptors. Compared to the state-of-the-art, SliceMatch achieves a 19% lower median localization error on the VIGOR benchmark using the same VGG16 backbone at 150 frames per second, and a 50% lower error when using a ResNet50 backbone

SliceNet: Street-to-Satellite Image Metric Localization using Local Feature Matching

This work addresses visual localization for intelligent vehicles. The task of cross-view matching-based localization is to estimate the geo-location of a vehicle-mounted camera by matching the captured street view image with an overhead-view satellite map containing the vehicle's local surroundings. This local satellite view image can be obtained using any rough localization prior, e.g., from a global navigation satellite system or temporal filtering. Existing cross-view matching methods are global image descriptor-based and achieve considerably lower localization performance than structure-based methods with 3D maps. Whereas structure-based methods utilized global image descriptors in the past, recent structure-based work has shown that significantly better localization performance can be achieved using local image descriptors to find pixel-level correspondences between the query street view image and the 3D map. Hence, using local image descriptors may be the key to improving the localization performance of cross-view matching methods. However, the street and the satellite view do exhibit not only very different visual appearances but also have distinctive geometric configurations. As a result, finding correspondences between the two views is not a trivial task. We observe that the geometric relationship between the street and satellite view implies that every vertical line in the street view image has a corresponding azimuth direction in the satellite view image. Based on this prior, we devise a novel neural network architecture called SliceNet that extracts local image descriptors from both images and matches these to compute a dense spatial distribution for the camera's location. Specifically, the geometric prior is used as a weakly supervised signal to enable SliceNet to learn the correspondences between the two views. As an additional task, we also show that the extracted local image descriptors can be used to determine the heading of the camera. SliceNet outperforms global image descriptor-based cross-view matching methods and achieves state-of-the-art localization results on the VIGOR dataset. Notably, the proposed method reduces the median metric localization error by 21% and 4% compared to the state-of-the-art methods when generalizing, respectively, in the same area and across areas.Mechanical Engineerin

The outcomes of hypoglossal nerve stimulation in the management of OSA: A systematic review and meta-analysis

Objectives: Obstructive sleep apnea (OSA) is a prevalent disease with significant health impacts. While first line therapy is CPAP, long-term compliance is low and device misuse is common, highlighting the need for alternative therapies. Upper airway surgery is one alternative, but substantial side effects hamper efficacy. A new alternative is an implantable hypoglossal nerve stimulator (HNS). These devices utilize neuromodulation to dilate/reinforce the airway and reduce side effects associated with traditional surgery. Several recent trials investigated the efficacy of these devices. The purpose of this study was to perform meta-analysis of available HNS studies investigating treatment of OSA to analyze objective and subjective outcomes and side effects. Methods: A comprehensive literature search of PubMed and Scopus was performed. Two independent reviewers examined clinical trials investigating HNS in treatment of sleep apnea in adults. Studies with objective and subjective endpoints in sleep were included for analysis. Adverse events from trials were also recorded. Results: Across 16 studies, 381 patients were analyzed. At 6 months (p = 0.008), mean SAQLI improved by 3.1 (95%CI, 2.6–3.7). At 12 months (p < 0.0001), mean AHI was reduced by 21.1 (95%CI, 16.9–25.3), mean ODI was reduced by 15.0 (95%CI, 12.7–17.4), mean ESS was reduced by 5.0 (95%CI, 4.2–5.8), mean FOSQ improved by 3.1 (95%CI, 2.6–3.4). Pain (6.2%:0.7–16.6), tongue abrasion (11.0%:1.2–28.7), and internal (3.0%:0.3–8.4)/external device (5.8%:0.3–17.4) malfunction were common adverse events. Conclusions: HNS is a safe and effective treatment for CPAP refractory OSA. Further study comparing HNS to other therapies is required. Keywords: Surgical treatment of obstructive sleep apnea, Sleep medicine, Obstructive sleep apne