We propose a novel, large-scale, structure-from-motion framework that advances the state of the art in data scalability from city-scale modeling (millions of images) to world-scale modeling (several tens of millions of images) using just a single computer. The main enabling technology is the use of a streaming-based framework for connected component discovery. Moreover, our system employs an adaptive, online, iconic image clustering approach based on an augmented bag-of-words representation, in order to balance the goals of registration, comprehensiveness, and data compactness. We demonstrate our proposal by operating on a recent publicly available 100 million image crowd-sourced photo collection containing images geographically distributed throughout the entire world. Results illustrate that our streaming-based approach does not compromise model completeness, but achieves unprecedented levels of efficiency and scalability.

Structure-from-motion (SFM) is widely utilized to generate 3D reconstructions from unordered photo-collections. However, in the presence of non unique, symmetric, or otherwise indistinguishable structure, SFM techniques often incorrectly reconstruct the final model. We propose a method that not only determines if an error is present, but automatically corrects the error in order to produce a correct representation of the scene. We find that by exploiting the co-occurrence information present in the scene's geometry, we can successfully isolate the 3D points causing the incorrect result. This allows us to split an incorrect reconstruction into error-free sub-models that we then correctly merge back together. Our experimental results show that our technique is efficient, robust to a variety of scenes, and outperforms existing methods.

Of late, threats enabled by the ubiquitous use of mobile devices have drawn much interest from the research community. However, prior threats all suffer from a similar, and profound, weakness - namely the requirement that the adversary is either within visual range of the victim (e.g., to ensure that the pop-out events in reflections in the victim's sunglasses can be discerned) or is close enough to the target to avoid the use of expensive telescopes. In this paper, we broaden the scope of the attacks by relaxing these requirements and show that breaches of privacy are possible even when the adversary is around a corner. The approach we take overcomes challenges posed by low image resolution by extending computer vision methods to operate on small, high-noise, images. Moreover, our work is applicable to all types of keyboards because of a novel application of fingertip motion analysis for key-press detection. In doing so, we are also able to exploit reflections in the eyeball of the user or even repeated reflections (i.e., a reflection of a reflection of the mobile device in the eyeball of the user). Our empirical results show that we can perform these attacks with high accuracy, and can do so in scenarios that aptly demonstrate the realism of this threat.