The Internet has become an unprecedented source of visual information about our world,
with millions of people uploading photos and videos to media-sharing sites at staggering …

This work explores the use of spatial context as a source of free and plentiful supervisory
signal for training a rich visual representation. Given only a large, unlabeled image …

Many data sets can be viewed as a noisy sampling of an underlying space, and tools from
topological data analysis can characterize this structure for the purpose of knowledge …

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 …

GPS (for Graph Processing System) is a complete open-source system we developed for
scalable, fault-tolerant, and easy-to-program execution of algorithms on extremely large …

In recent years, the computer graphics and computer vision communities have devoted
significant attention to research based on Internet visual media resources. The huge number …

SIFT-like local feature descriptors are ubiquitously employed in computer vision applications
such as content-based retrieval, video analysis, copy detection, object recognition, photo …

Given a set of poorly aligned images of the same visual concept without any annotations, we
propose an algorithm to jointly bring them into pixel-wise correspondence by estimating a …

We present a deep generative scene modeling technique for indoor environments. Our goal
is to train a generative model using a feed-forward neural network that maps a prior …

Large collections of 3D models from the same object class (eg, chairs, cars, animals) are
now commonly available via many public repositories, but exploring the range of shape …