Abstract

We address the problem of large scale place-of-interest recog- nition in cell phone images of urban scenarios. Here, we go beyond what has been shown in earlier approaches by exploiting the nowadays often available 3D building information (e.g. from extruded floor plans) and massive street-view like image data for database creation. Exploiting van- ishing points in query images and thus fully removing 3D rotation from the recognition problem allows then to simplify the feature invariance to a pure homothetic problem, which we show leaves more discriminative power in feature descriptors than classical SIFT. We rerank visual word based document queries using a fast stratified homothetic verification that is tailored for repetitive patterns like window grids on facades and in most cases boosts the correct document to top positions if it was in the short list. Since we exploit 3D building information, the approach finally outputs the camera pose in real world coordinates ready for aug- menting the cell phone image with virtual 3D information. The whole system is demonstrated to outperform traditional approaches on city scale experiments for different sources of street-view like image data and a challenging set of cell phone images.