Abstract. This paper introduces an approach for dense 3D reconstruction from unregistered Internet-scale photo collections with about 3 million images within the span of a day on a single PC ("cloudless"). Our method advances image clustering, stereo, stereo fusion and structure from motion to achieve high computational performance. We leverage geometric and appearance constraints to obtain a highly parallel implementation on modern graphics processors and multi-core architectures. This leads to two orders of magnitude higher performance on an order of magnitude larger dataset than competing state-of-the-art approaches.

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Hierarchical stereo provides an efficient coarse-to-fine mechanism for disparity map estimation. However, common drawbacks of such an approach include the loss of high frequency structures not observable at coarse scale levels, as well as the unrecoverable propagation of erroneous disparity estimates through the scale space. This paper presents an adaptive scale selection mechanism to determine a suitable resolution level from which to begin the hierarchical depth estimation process for each pixel. The proposed scale selection mechanism allows us to robustly implement variable cost aggregation in order to reduce the variability of the photo-consistency measure across scale space. We also incorporate a weighted shiftable window mechanism to enable error correction during coarse-to-fine depth refinement. Experiments illustrate the effectiveness of our approach in terms of disparity accuracy, while attaining a computational efficiency compromise between full resolution and hierarchical disparity map estimation.

Figure 1: A short segment of a long multi-perspective panorama generated by our system from a video sequence of a street scene. AbstractIn this paper, we present an efficient technique for generating multi-perspective panoramic images of long scenes. The input to our system is a video sequence captured by a moving camera navigating through a long scene, and our goal is to efficiently generate a panoramic summary of the scene. This problem has received considerable attention in recent years, leading to the development of a number of systems capable of generating high-quality panoramas. However, a significant limitation of current systems is their computational complexity: most current techniques employ computationally expensive algorithms (such as structurefrom-motion and dense stereo), or require some degree of manual interaction. In turn, this limits the scalability of the algorithms as well as their ease of implementation. In contrast, the technique we present is simple, efficient, easy to implement, and produces results of comparable quality to state of the art techniques, while doing so at a fraction of the computational cost. Our system operates entirely in the 2D image domain, performing robust image alignment and optical flow based mosaicing, in lieu of more expensive 3D pose/structure computation. We demonstrate the effectiveness of our system on a number of challenging image sequences.