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

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Figure 2: Images downloaded form the the Internet by searching for “Statue of Liberty”, which do not represent the Statue of Liberty in New York. for salient feature points to neighboring views. In the case of video these correspondences are determined through KLT-tracking on the GPU with simultaneous estimation of the camera gain (Section 4.1.1). For Internet photo collections our system first establishes a spatial neighborhood relationship for each image through a clustering of single image descriptors, which effectively clusters close viewpoints. The feature points used are the SIFT features [58] due to their enhanced invariance to larger viewpoint changes (Section 4.1). • Camera pose/motion estimation from local correspondences is robustly performed through an efficient RANSAC technique called ARRSAC (Section 4.1.2). It determines the inlier correspondences and the camera position with respect to the previous image and the known model. 10
In the case of available GPS data our system uses the local correspondences to perform sensor fusion with the GPS and INS data 4.1.3. • Global Correspondence estimation is performed to enable global drift correction. This step searches beyond the temporal neighbors in video and beyond the neighbors in the cluster for frames with overlap to the current frame (Section 4.2). • Bundle adjustment uses the global correspondences to reduce the accumulated drift of the camera registrations from the local camera pose estimates (Section 4.3). • Dense geometry estimation in real-time is performed by our 3D reconstruction from video system to extract the depth of all pixels from the camera (Section 5). Our system uses a two stage estimation strategy. First we use efficient GPU based stereo to determine the depth map for every frame. Then we use the temporal redundancy of the stereo estimations to filter out erroneous depth estimates and fuse the correct depth estimates. • Model extraction is performed to extract a triangular mesh representing the scene from the depth maps. The next section will first introduce our algorithm for real-time 3D reconstruction from video. Afterwards in Section 4.1 we will detail the differences from our algorithm for camera registration of Internet photo collections. 11
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Page 39 and 40: [30] Y. Jing, S. Baluja, H. Rowley,
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