We present a two-stage, geometry-aware approach for matching SIFT-like features in a fast and reliable manner. Our approach first uses a small sample of features to estimate the epipolar geometry between the images and leverages it for guided matching of the remaining features. This simple and generalized two-stage matching approach produces denser feature correspondences while allowing us to formulate an accelerated search strategy to gain significant speedup over the traditional matching. The traditional matching punitively rejects many true feature matches due to a global ratio test. The adverse effect of this is particularly visible when matching image pairs with repetitive structures. The geometry-aware approach prevents such preemptive rejection using a selective ratio-test and works effectively even on scenes with repetitive structures. We also show that the proposed algorithm is easy to parallelize and implement it on the GPU. We experimentally validate our algorithm on publicly available datasets and compare the results with state-of-the-art methods.

In this paper, we present a new multistage approach for SfM reconstruction of a single component. Our method begins with building a coarse 3D reconstruction using highscale features of given images. This step uses only a fraction of features and is fast. We enrich the model in stages by localizing remaining images to it and matching and triangulating remaining features. Unlike traditional incremental SfM, localization and triangulation steps in our approach are made efficient and embarrassingly parallel using geometry of the coarse model. The coarse model allows us to use 3D-2D correspondences based direct localization techniques to register remaining images. We further utilize the geometry of the coarse model to reduce the pair-wise image matching effort as well as to perform fast guided feature matching for majority of features. Our method produces similar quality models as compared to incremental SfM methods while being notably fast and parallel. Our algorithm can reconstruct a 1000 images dataset in 15 hours using a single core, in about 2 hours using 8 cores and in a few minutes by utilizing full parallelism of about 200 cores.

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View-graph is an essential input to large-scale structure from motion (SfM) pipelines. Accuracy and efficiency of large-scale SfM is crucially dependent on the input viewgraph. Inconsistent or inaccurate edges can lead to inferior or wrong reconstruction. Most SfM methods remove 'undesirable' images and pairs using several, fixed heuristic criteria, and propose tailor-made solutions to achieve specific reconstruction objectives such as efficiency, accuracy, or disambiguation. In contrast to these disparate solutions, we propose a single optimization framework that can be used to achieve these different reconstruction objectives with task-specific cost modeling. We also construct a very efficient network-flow based formulation for its approximate solution. The abstraction brought on by this selection mechanism separates the challenges specific to datasets and reconstruction objectives from the standard SfM pipeline and improves its generalization. This paper demonstrates the application of the proposed view-graph framework with standard SfM pipeline for two particular use-cases, (i) accurate and ghost-free reconstructions of highly ambiguous datasets using costs based on disambiguation priors, and (ii) accurate and efficient reconstruction of large-scale Internet datasets using costs based on commonly used priors.