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With recent advances in mobile computing, the demand for visual localization or landmark identification on mobile devices is gaining interest. We advance the state of the art in this area by fusing two popular representations of street-level image data—facade-aligned and viewpoint-aligned— and show that they contain complementary information that can be(More)
We survey popular data sets used in computer vision literature and point out their limitations for mobile visual search applications. To overcome many of the limitations, we propose the Stanford Mobile Visual Search data set. The data set contains camera-phone images of products, CDs, books, outdoor landmarks, business cards, text documents, museum(More)
This paper presents a novel method to process large scale, ground level Light Detection and Ranging (LIDAR) data to automatically detect geo-referenced navigation attributes (traffic signs and lane markings) corresponding to a collection travel path. A mobile data collection device is introduced. Both the intensity of the LIDAR light return and 3-D(More)
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