We present a system for interactively browsing and exploring large unstructured collections of photographs of a scene using a novel 3D interface. Our system consists of an image-based modeling front end that automatically computes the viewpoint of each photograph as well as a sparse 3D model of the scene and image to model correspondences. Our photo explorer uses image-based rendering techniques to smoothly transition between photographs, while also enabling full 3D navigation and exploration of the set of images and world geometry, along with auxiliary information such as overhead maps. Our system also makes it easy to construct photo tours of scenic or historic locations, and to annotate image details, which are automatically transferred to other relevant images. We demonstrate our system on several large personal photo collections as well as images gathered from Internet photo sharing sites.

There are billions of photographs on the Internet, comprising the largest and most diverse photo collection ever assembled. How can computer vision researchers exploit this imagery? This paper explores this question from the standpoint of 3D scene modeling and visualization. We present structure-from-motion and image-based rendering algorithms that operate on hundreds of images downloaded as a result of keyword-based image search queries like “Notre Dame ” or “Trevi Fountain.” This approach, which we call Photo Tourism, has enabled reconstructions of numerous well-known world sites. This paper presents these algorithms and results as a first step towards 3D modeling of the world’s well-photographed sites, cities, and landscapes from Internet imagery, and discusses key open problems and challenges for the research community.

Figure 1: Organizing image collections. (a) Typical unorganized display of thumbnails. (b) Images organized according to camera positions (GPS). (c) The proposed hierarchical organization allows meaningful image browsing according to scene semantics. Note that images of the same object may appear at random positions in (a), in different positions based on the camera location in (b) but are placed in the same sub-tree in (c). (c) We present a novel framework for organizing large collections of images in a hierarchical way, based on scene semantics. Rather than score images directly, we use them to score the scene in order to identify typical views and important locations which we term Geo-Relevance. This is done by relating each image with its viewing frustum which can be readily computed for huge collections of images nowadays. The frustum contains much more information than only camera position that has been used so far. For example, it distinguishes between a photo of the Eiffel Tower and a photo of a garbage bin taken from the exact same place. The proposed framework enables a summarized display of the information and facilitates efficient browsing.

Wireless networks of visual sensors have recently emerged as a new type of sensor-based intelligent system, with performance and complexity challenges that go beyond that of existing wireless sensor networks. The goal of the visual sensor network we examine is to provide a user with visual information from any arbitrary viewpoint within the monitored field. This can be accomplished by synthesizing image data from a selection of cameras whose fields of view overlap with the desired field of view. In this work, we compare two methods for the selection of the camera-nodes. The first method selects cameras that minimize the difference between the images provided by the selected cameras and the image that would be captured by a real camera from the desired viewpoint. The second method considers the energy limitations of the battery powered camera-nodes, as well as their importance in the 3D coverage preservation task. Simulations using both metrics for camera-node selection show a clear trade-off between the quality of the reconstructed image and the network’s ability to provide full coverage of the monitored 3D space for a longer period of time. 1.

We are rapidly moving toward a world where personal networked video cameras are ubiquitous. Already, camera-equipped cell phones are becoming commonplace. Imagine being able to tap into all of these live video feeds to remotely explore the world in real-time. We introduce RealityFlythrough, a telepresence system that makes this vision possible. By situating live 2d video feeds in a 3d model of the world, RealityFlythrough allows any space to be explored remotely. No special cameras, tripods, rigs, scaffolding, or lighting is required to create the model, and no lengthy preprocessing of images is necessary. Rather than try to achieve photorealism at every point in space, we instead focus on providing the user with a sense of how the video streams relate to one another spatially. By providing cues in the form of dynamic transitions, we can approximate photorealistic telepresence while harnessing cameras “in the wild. ” This paper describes the RealityFlythrough system, and reports on a live flythrough experience. We find that telepresence can work in the wild using only commodity hardware and off-the-shelf software, and that imperfect transitions are sensible and provide a compelling user experience. 1

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Visual sensor networks have emerged as an important class of sensor-based distributed intelligent systems, with unique performance, complexity, and quality of service challenges. Consisting of a large number of low-power camera nodes, visual sensor networks support a great number of novel vision-based applications. The camera nodes provide information from a monitored site, performing distributed and collaborative processing of their collected data. Using multiple cameras in the network provides different views of the scene, which enhances the reliability of the captured events. However, the large amount of image data produced by the cameras combined with the network’s resource constraints require exploring new means for data processing, communication, and sensor management. Meeting these challenges of visual sensor networks requires interdisciplinary approaches, utilizing vision processing, communications and networking, and embedded processing. In this paper, we provide an overview of the current state-of-the-art in the field of visual sensor networks, by exploring several relevant research directions. Our goal is to provide a better understanding of current research problems in the different research fields of visual sensor networks, andtoshowhowthesedifferent research fields should interact to solve the many challenges of visual sensor networks. Copyright © 2009 S. Soro and W. Heinzelman. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1.