Beyond Geo-localization: Fine-grained Orientation of Street-view Images by Cross-view Matching with Satellite Imagery with Supplementary Materials
National University of Singapore（新加坡国立大学）；Institute for Infocomm Research, A*STAR（新加坡科技研究局资讯通信研究院）；Grab Chronos S.R.L.（Grab Chronos有限责任公司）；Grabtaxi Holdings Pte. Ltd.（Grab出租车控股私人有限公司）
Wenmiao Hu, Yichen Zhang, Yuxuan Liang, Yifang Yin, Andrei Georgescu, An Tran, Hannes Kruppa, See-Kiong Ng, Roger Zimmermann
备注：
This paper has been accepted by ACM Multimedia 2022. This version contains additional supplementary materials
发表信息：
Proceedings of the 30th ACM International Conference on Multimedia (2022) 6155-6164
Street-view imagery provides us with novel experiences to explore different places remotely. Carefully calibrated street-view images (e.g. Google Street View) can be used for different downstream tasks, e.g. navigation, map features extraction. As personal high-quality cameras have become much more affordable and portable, an enormous amount of crowdsourced street-view images are uploaded to the internet, but commonly with missing or noisy sensor information. To prepare this hidden treasure for "ready-to-use" status, determining missing location information and camera orientation angles are two equally important tasks. Recent methods have achieved high performance on geo-localization of street-view images by cross-view matching with a pool of geo-referenced satellite imagery. However, most of the existing works focus more on geo-localization than estimating the image orientation. In this work, we re-state the importance of finding fine-grained orientation for street-view images, formally define the problem and provide a set of evaluation metrics to assess the quality of the orientation estimation. We propose two methods to improve the granularity of the orientation estimation, achieving 82.4% and 72.3% accuracy for images with estimated angle errors below 2 degrees for CVUSA and CVACT datasets, corresponding to 34.9% and 28.2% absolute improvement compared to previous works. Integrating fine-grained orientation estimation in training also improves the performance on geo-localization, giving top 1 recall 95.5%/85.5% and 86.8%/80.4% for orientation known/unknown tests on the two datasets.
PetalView: Fine-grained Location and Orientation Extraction of Street-view Images via Cross-view Local Search with Supplementary Materials
Grab-NUS AI Lab（Grab-NUS人工智能实验室）；National University of Singapore（新加坡国立大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Institute for Infocomm Research, A*STAR（新加坡资讯通信研究院（新加坡科技研究局下属））；Grabtaxi Holdings Pte. Ltd.（GrabTaxi控股私人有限公司）；Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究院）
ETH Zürich（苏黎世联邦理工学院）；US Department of Agriculture（美国农业部）；University of Edinburgh（爱丁堡大学）；California Institute of Technology（加州理工学院）
We introduce an approach for updating older tree inventories with geographic coordinates using street-level panorama images and a global optimization framework for tree instance matching. Geolocations of trees in inventories until the early 2000s where recorded using street addresses whereas newer inventories use GPS. Our method retrofits older inventories with geographic coordinates to allow connecting them with newer inventories to facilitate long-term studies on tree mortality etc. What makes this problem challenging is the different number of trees per street address, the heterogeneous app…
The goal of cross-view image based geo-localization is to determine the location of a given street view image by matching it against a collection of geo-tagged satellite images. This task is notoriously challenging due to the drastic viewpoint and appearance differences between the two domains. We show that we can address this discrepancy explicitly by learning to synthesize realistic street views from satellite inputs. Following this observation, we propose a novel multi-task architecture in which image synthesis and retrieval are considered jointly. The rationale behind this is that we can b…
Australian National University（澳大利亚国立大学）；University of Oxford（牛津大学）；University of Technology Sydney（悉尼科技大学）
This paper presents a new approach for synthesizing a novel street-view panorama given an overhead satellite image. Taking a small satellite image patch as input, our method generates a Google's omnidirectional street-view type panorama, as if it is captured from the same geographical location as the center of the satellite patch. Existing works tackle this task as an image generation problem which adopts generative adversarial networks to implicitly learn the cross-view transformations, while ignoring the domain relevance. In this paper, we propose to explicitly establish the geometric corres…
Northwestern Polytechnical University（西北工业大学）；Australian Centre for Robotic Vision（澳大利亚机器人视觉中心）
This paper is concerned with the problem of how to better exploit 3D geometric information for dense semantic image labeling. Existing methods often treat the available 3D geometry information (e.g., 3D depth-map) simply as an additional image channel besides the R-G-B color channels, and apply the same technique for RGB image labeling. In this paper, we demonstrate that directly performing 3D convolution in the framework of a residual connected 3D voxel top-down modulation network can lead to superior results. Specifically, we propose a 3D semantic labeling method to label outdoor street scen…
University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）
Street-to-aerial image geo-localization, which matches a query street-view image to the GPS-tagged aerial images in a reference set, has attracted increasing attention recently. In this paper, we revisit this problem and point out the ignored issue about image alignment information. We show that the performance of a simple Siamese network is highly dependent on the alignment setting and the comparison of previous works can be unfair if they have different assumptions. Instead of focusing on the feature extraction under the alignment assumption, we show that improvements in metric learning tech…
ADAPT Centre（ADAPT中心）；School of Computer Science and Statistics, Trinity College Dublin（都柏林圣三一大学计算机科学与统计学学院）
Many applications such as autonomous navigation, urban planning and asset monitoring, rely on the availability of accurate information about objects and their geolocations. In this paper we propose to automatically detect and compute the GPS coordinates of recurring stationary objects of interest using street view imagery. Our processing pipeline relies on two fully convolutional neural networks: the first segments objects in the images while the second estimates their distance from the camera. To geolocate all the detected objects coherently we propose a novel custom Markov Random Field model…
Spatially-Weighted CLIP for Street-View Geo-localization
School of Geospatial Engineering and Science, Sun Yat-Sen University（中山大学地理科学与规划学院）；School of Geographical and Earth Sciences, University of Glasgow（格拉斯哥大学地理与地球科学学院）；School of System Science and Engineering, Sun Yat-Sen University（中山大学系统科学与工程学院）
ETH Zurich（苏黎世联邦理工学院）；The University of Tokyo（东京大学）；Zhejiang University（浙江大学）；The Ohio State University（俄亥俄州立大学）；Microsoft（微软）
We present a novel method for synthesizing both temporally and geometrically consistent street-view panoramic video from a single satellite image and camera trajectory. Existing cross-view synthesis approaches focus on images, while video synthesis in such a case has not yet received enough attention. For geometrical and temporal consistency, our approach explicitly creates a 3D point cloud representation of the scene and maintains dense 3D-2D correspondences across frames that reflect the geometric scene configuration inferred from the satellite view. As for synthesis in the 3D space, we impl…
The Alan Turing Institute（艾伦·图灵研究所）；University College London（伦敦大学学院）
Recent researches have shown the increasing use of machine learn-ing methods in geography and urban analytics, primarily to extract features and patterns from spatial and temporal data using a supervised approach. Researches integrating geographical processes in machine learning models and the use of unsupervised approacheson geographical data for knowledge discovery had been sparse. This research contributes to the ladder, where we show how latent variables learned from unsupervised learning methods on urbanimages can be used for geographic knowledge discovery. In particular, we propose a sim…
GeoReasoner: Geo-localization with Reasoning in Street Views using a Large Vision-Language Model
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Tongji University（同济大学）；Independent Researcher（独立研究者）；Information Engineering University（信息工程大学）
DM-KG: A Novel Method for Boosting Spatial Cognition of Vision-Language Models in Street View Imagery
Institute of Surveying and Mapping, Information Engineering University（信息工程大学测绘学院）；Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences（中国科学院地理科学与资源研究所）
Fine-Grained Building Function Recognition from Street-View Images via Geometry-Aware Semi-Supervised Learning
School of Geospatial Engineering and Science, Sun Yat-sen University（中山大学地理空间工程与科学学院）；Tsinghua University（清华大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；SenseTime Research（商汤科技研究院）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）
University of Washington（华盛顿大学）；Google Research（谷歌研究院）；Stanford University（斯坦福大学）
We present a framework for automatically reconfiguring images of street scenes by populating, depopulating, or repopulating them with objects such as pedestrians or vehicles. Applications of this method include anonymizing images to enhance privacy, generating data augmentations for perception tasks like autonomous driving, and composing scenes to achieve a certain ambiance, such as empty streets in the early morning. At a technical level, our work has three primary contributions: (1) a method for clearing images of objects, (2) a method for estimating sun direction from a single image, and (3…
Cross-Modal Urban Sensing: Evaluating Sound-Vision Alignment Across Street-Level and Aerial Imagery
Department of Geography, University of South Carolina（南卡罗来纳大学地理系）；Department of Environmental Sciences, Emory University（埃默里大学环境科学系）；University of Canterbury（坎特伯雷大学）；School of Resource and Environmental Sciences, Wuhan University（武汉大学资源与环境科学学院）
Cross-View Geo-Localization with Street-View and VHR Satellite Imagery in Decentrality Settings
School of Remote Sensing and Information Engineering, Wuhan University（武汉大学遥感信息工程学院）；Ant Group（蚂蚁集团）；Technology Innovation Center for Collaborative Applications of Natural Resources Data in GBA, Ministry of Natural Resources（自然资源部粤港澳自然资源数据协同应用技术创新中心）
Accurately maintaining digital street maps is labor-intensive. To address this challenge, much work has studied automatically processing geospatial data sources such as GPS trajectories and satellite images to reduce the cost of maintaining digital maps. An end-to-end map update system would first process geospatial data sources to extract insights, and second leverage those insights to update and improve the map. However, prior work largely focuses on the first step of this pipeline: these map extraction methods infer road networks from scratch given geospatial data sources (in effect creatin…
Neural Radiance Fields (NeRFs) aim to synthesize novel views of objects and scenes, given the object-centric camera views with large overlaps. However, we conjugate that this paradigm does not fit the nature of the street views that are collected by many self-driving cars from the large-scale unbounded scenes. Also, the onboard cameras perceive scenes without much overlapping. Thus, existing NeRFs often produce blurs, 'floaters' and other artifacts on street-view synthesis. In this paper, we propose a new street-view NeRF (S-NeRF) that considers novel view synthesis of both the large-scale bac…
In this paper, based on the assumption that the object boundaries (e.g., buildings) from the over-view data should coincide with footprints of façade 3D points generated from street-view photogrammetric images, we aim to address this problem by proposing a fully automated geo-registration method for cross-view data, which utilizes semantically segmented object boundaries as view-invariant features under a global optimization framework through graph-matching: taking the over-view point clouds generated from stereo/multi-stereo satellite images and the street-view point clouds generated from mon…
Studies evaluating bikeability usually compute spatial indicators shaping cycling conditions and conflate them in a quantitative index. Much research involves site visits or conventional geospatial approaches, and few studies have leveraged street view imagery (SVI) for conducting virtual audits. These have assessed a limited range of aspects, and not all have been automated using computer vision (CV). Furthermore, studies have not yet zeroed in on gauging the usability of these technologies thoroughly. We investigate, with experiments at a fine spatial scale and across multiple geographies (S…
SkyLink: Unifying Street-Satellite Geo-Localization via UAV-Mediated 3D Scene Alignment
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（多媒体可信感知与高效计算重点实验室，教育部，厦门大学）；School of Electronic and Information Engineering, Foshan University（电子与信息工程学院，佛山大学）
After a disaster, teams of structural engineers collect vast amounts of images from damaged buildings to obtain lessons and gain knowledge from the event. Images of damaged buildings and components provide valuable evidence to understand the consequences on our structures. However, in many cases, images of damaged buildings are often captured without sufficient spatial context. Also, they may be hard to recognize in cases with severe damage. Incorporating past images showing a pre-disaster condition of such buildings is helpful to accurately evaluate possible circumstances related to a buildin…
UrbanVGGT: Scalable Sidewalk Width Estimation from Street View Images
Heinz College of Information Systems and Public Policy（信息系统与公共政策学院）；Carnegie Mellon University（卡内基梅隆大学）；Institute of Remote Sensing and Geographical Information System（遥感与地理信息系统研究所）
IRISA institute, Université Bretagne Sud（IRISA研究所，南布列塔尼大学）；MultiModal Remote Sensing group, University of Zurich（苏黎世大学多模态遥感研究组）；Laboratory of GeoInformation Science and Remote Sensing, Wageningen University & Research（瓦赫宁根大学及研究中心地理信息科学与遥感实验室）；Photogrammetry and Remote Sensing group, ETH Zurich（苏黎世联邦理工学院摄影测量与遥感研究组）；G2C institute, University of Applied Sciences, Western Switzerland（瑞士西部应用科学与艺术大学G2C研究所）
In this paper, we discuss and review how combined multi-view imagery from satellite to street-level can benefit scene analysis. Numerous works exist that merge information from remote sensing and images acquired from the ground for tasks like land cover mapping, object detection, or scene understanding. What makes the combination of overhead and street-level images challenging, is the strongly varying viewpoint, different scale, illumination, sensor modality and time of acquisition. Direct (dense) matching of images on a per-pixel basis is thus often impossible, and one has to resort to altern…
Designing streetscapes from street-view imagery using diffusion models
Department of Urban and Regional Planning, University of Florida（城市与区域规划系，佛罗里达大学）；Singapore-MIT Alliance for Research and Technology Centre (SMART)（新加坡-麻省理工联合研究中心（SMART））；Department of Landscape Architecture and Urban Planning, Texas A&M University（景观建筑与城市规划系，德克萨斯大学安德森分校）；Department of Agronomy, University of Florida（农业系，佛罗里达大学）
Geo-localizing static objects from street images is challenging but also very important for road asset mapping and autonomous driving. In this paper we present a two-stage framework that detects and geolocalizes traffic signs from low frame rate street videos. Our proposed system uses a modified version of RetinaNet (GPS-RetinaNet), which predicts a positional offset for each sign relative to the camera, in addition to performing the standard classification and bounding box regression. Candidate sign detections from GPS-RetinaNet are condensed into geolocalized signs by our custom tracker, whi…
From Satellite to Street: A Hybrid Framework Integrating Stable Diffusion and PanoGAN for Consistent Cross-View Synthesis
Department of Information and Computer Science, King Fahd University of Petroleum and Minerals（信息与计算机科学系，国王法赫德石油与矿物大学）；NCMI, CSIRO（CSIRO国家科学机构）；Department of Computer Science, National University of Computer and Emerging Sciences (FAST-NUCES)（计算机科学系，国家计算机与新兴科学大学（FAST-NUCES））；Faculty of Engineering and IT, University of Technology Sydney（工程与信息技术学院，技术大学悉尼）；Faculty of Science, University of Ontario Institute of Technology（科学学院， Ontario Institute of Technology大学）
针对卫星到街景跨视角合成中的视角与外观差异，本文提出融合 Stable Diffusion、条件 GAN 与双分支多阶段训练的混合框架，在 CVUSA 上提升几何一致性与视觉真实感。
Chinese University of Hong Kong（香港中文大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
The ability to recognize the position and order of the floor-level lines that divide adjacent building floors can benefit many applications, for example, urban augmented reality (AR). This work tackles the problem of locating floor-level lines in street-view images, using a supervised deep learning approach. Unfortunately, very little data is available for training such a network $-$ current street-view datasets contain either semantic annotations that lack geometric attributes, or rectified facades without perspective priors. To address this issue, we first compile a new dataset and develop a…
In this paper we aim to determine the location and orientation of a ground-level query image by matching to a reference database of overhead (e.g. satellite) images. For this task we collect a new dataset with one million pairs of street view and overhead images sampled from eleven U.S. cities. We explore several deep CNN architectures for cross-domain matching -- Classification, Hybrid, Siamese, and Triplet networks. Classification and Hybrid architectures are accurate but slow since they allow only partial feature precomputation. We propose a new loss function which significantly improves th…
Royal Melbourne Institute of Technology（皇家墨尔本理工大学）
On-road bicycle lanes improve safety for cyclists, and encourage participation in cycling for active transport and recreation. With many local authorities responsible for portions of the infrastructure, official maps and datasets of bicycle lanes may be out-of-date and incomplete. Even "crowdsourced" databases may have significant gaps, especially outside popular metropolitan areas. This thesis presents a method to create a map of bicycle lanes in a survey area by taking sample street scene images from each road, and then applying a deep learning model that has been trained to recognise bicycl…
Universitat Autònoma de Barcelona (UAB)（巴塞罗那自治大学）；Daimler AG R&D（戴姆勒股份公司研发部）；Computer Vision Center (CVC)（计算机视觉中心）；Element AI；ETH Zürich（苏黎世联邦理工学院）
This work presents and evaluates a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather restrictive geometric assumptions for Stixels by introducing a novel depth model to account for non-flat roads and slanted objects. Both semantic and depth cues are used jointly to infer the scene representation in a sound global energy minimization formulation. Furthermore, a novel approximation scheme is introduced in order to significantly reduce the computational complexity of the Stixel algorithm, and then achieve…
Street-Level Geolocalization Using Multimodal Large Language Models and Retrieval-Augmented Generation
Vocational School of Social Sciences, Marmara University（马尔马拉大学社会科学职业学校）；Geospatial Data Science Group, School of Geographical & Earth Sciences, University of Glasgow（格拉斯哥大学地理与地球科学学院空间数据科学小组）
SVII-3D: Advancing Roadside Infrastructure Inventory with Decimeter-level 3D Localization and Comprehension from Sparse Street Imagery
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China（信息工程测绘遥感国家重点实验室（LIESMARS），武汉大学）；Research Institute Ltd, Chengdu 610000, China（四川省公路规划设计研究有限公司）
Search with local intent is becoming increasingly useful due to the popularity of the mobile device. The creation and maintenance of accurate listings of local businesses worldwide is time consuming and expensive. In this paper, we propose an approach to automatically discover businesses that are visible on street level imagery. Precise business store front detection enables accurate geo-location of businesses, and further provides input for business categorization, listing generation, etc. The large variety of business categories in different countries makes this a very challenging problem. M…
From Street to Orbit: Training-Free Cross-View Retrieval via Location Semantics and LLM Guidance
Ulsan National Institute of Science and Technology (UNIST)（蔚山科学技术院）；Electronics and Telecommunications Research Institute (ETRI)（电子通信研究院）；Kyungpook National University（庆北大学）
Determining the exact latitude and longitude that a photo was taken is a useful and widely applicable task, yet it remains exceptionally difficult despite the accelerated progress of other computer vision tasks. Most previous approaches have opted to learn a single representation of query images, which are then classified at different levels of geographic granularity. These approaches fail to exploit the different visual cues that give context to different hierarchies, such as the country, state, and city level. To this end, we introduce an end-to-end transformer-based architecture that exploi…
