Fast Robust Large-scale Mapping from Video and Internet Photo ...

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exhaustive computation of all two-view relationships in the dataset. Using the independence of the pairwise evaluation Agarwal et al. [55] address this computational challenge by using a computing cluster with up to 500 cores to reduce the computation time significantly. Similar to our system they use image recognition techniques like approximate nearest neighbor search [56] and query expansion [57] to reduce the number of candidate relations. In contrast, our system uses single image descriptors to efficiently identify correlating images leading to an even smaller number of candidates. Images can then be registered on a single computation core in time comparable to the 500 cores of Agarwal et al. 3. Overview In this paper we describe our system for real-time computation of 3D models from ground reconnaissance video and for efficient 3D reconstruction from Internet photo collections downloaded from photo sharing web sites like Flickr 1 . The design of the system aims at efficient reconstruction through parallelization of the algorithms used enabling their execution on the GPU. The processing task for each type of input algorithmically share a large number of methods with some adaptation accounting for the significantly different characteristics of the data sources. In this section we will shortly discuss the different characteristics of the data sources and then introduce the main building blocks for the system. Our method for 30 Hz real-time reconstruction from video uses the video 1 www.flickr.com 8
streams of multiple cameras mounted on a vehicle to obtain a 3D reconstruction of the scene. Our system can operate from video alone, but additional sensors, such as differential GPS and an inertial sensor (INS), reduce accumulated error (drift) and provide registrations to the Earth’s coordinate system. Our system efficiently fuses the GPS, INS and camera measurements into an absolute camera position. Additionally, our system exploits the temporal order of the video frames, which implies a spatial relation ship of the frames, to efficiently perform the camera motion tracking and the dense geometry computation. In contrast to video, in Internet photo collections we encounter a high number of photos that do not share a rigid geometry with the images of the scene. These images are often mislabeled images from the data base, artistic images of the scene, duplicates, or parodies of the scene. An example for these different classes of unrelated images are shown in Figure 2. To quantify the amount of unrelated images in Internet photo collections we labeled a random subset for three downloaded datasets. Besides the dataset for the Statue of Liberty (47238 images with approximately 40% outliers) we downloaded two more datasets by searching for “San Marco” (45322 images with approximately 60 % outliers) and “Notre Dame” (11928 images with approximately 49%). The iconic clustering employed by our system can efficiently handle such high outlier ratios which is essential for handling large Internet photo collections. Despite the differences in the input data our systems share a common algorithmic structure as shown in Algorithm 1: • The local correspondence estimation establishes correspondences 9
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Page 39 and 40: [30] Y. Jing, S. Baluja, H. Rowley,
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