Large Scale SfM with the Distributed Camera Model

We introduce the distributed camera model, a novel model for Structure-from-Motion (SfM). This model describes image observations in terms of light rays with ray origins and directions rather than pixels. As such, the proposed model is capable of describing a single camera or multiple cameras simultaneously as the collection of all light rays observed. We show how the distributed camera model is a generalization of the standard camera model and describe a general formulation and solution to the absolute camera pose problem that works for standard or distributed cameras. The proposed method computes a solution that is up to 8 times more efficient and robust to rotation singularities in comparison with gDLS. Finally, this method is used in an novel large-scale incremental SfM pipeline where distributed cameras are accurately and robustly merged together. This pipeline is a direct generalization of traditional incremental SfM; however, instead of incrementally adding one camera at a time to grow the reconstruction the reconstruction is grown by adding a distributed camera. Our pipeline produces highly accurate reconstructions efficiently by avoiding the need for many bundle adjustment iterations and is capable of computing a 3D model of Rome from over 15,000 images in just 22 minutes.Comment: Published at 2016 3DV Conferenc

Working towards sustainable software for science: on the creation, maintenance and evaluation of open-source software

Alongside research papers and data, software is a vital research object. As more become confronted with its significance in the future of scientific discovery, a variety of opinions and philosophies are emerging over how to approach sustainable scientific software development. Matthew Turk provides background on his involvement in the Working towards Sustainable Software for Science: Practice and Experiences (WSSSPE) workshops and the launch of the special collection from the Journal of Open Research Software

The Formation of Population III Binaries from Cosmological Initial Conditions

Previous high resolution cosmological simulations predict the first stars to appear in the early universe to be very massive and to form in isolation. Here we discuss a cosmological simulation in which the central 50 solar mass clump breaks up into two cores, having a mass ratio of two to one, with one fragment collapsing to densities of 10^{-8} g/cc. The second fragment, at a distance of 800 astronomical units, is also optically thick to its own cooling radiation from molecular hydrogen lines, but is still able to cool via collision-induced emission. The two dense peaks will continue to accrete from the surrounding cold gas reservoir over a period of 10^5 years and will likely form a binary star system.Comment: Accepted by Science, first published online on July 9, 2009 in Science Express. 16 pages, 4 figures, includes supporting online materia

Machine Learning and Cosmological Simulations II: Hydrodynamical Simulations

We extend a machine learning (ML) framework presented previously to model galaxy formation and evolution in a hierarchical universe using N-body + hydrodynamical simulations. In this work, we show that ML is a promising technique to study galaxy formation in the backdrop of a hydrodynamical simulation. We use the Illustris Simulation to train and test various sophisticated machine learning algorithms. By using only essential dark matter halo physical properties and no merger history, our model predicts the gas mass, stellar mass, black hole mass, star formation rate, $g-r$ color, and stellar metallicity fairly robustly. Our results provide a unique and powerful phenomenological framework to explore the galaxy-halo connection that is built upon a solid hydrodynamical simulation. The promising reproduction of the listed galaxy properties demonstrably place ML as a promising and a significantly more computationally efficient tool to study small-scale structure formation. We find that ML mimics a full-blown hydrodynamical simulation surprisingly well in a computation time of mere minutes. The population of galaxies simulated by ML, while not numerically identical to Illustris, is statistically and physically robust and follows the same fundamental observational constraints. Machine learning offers an intriguing and promising technique to create quick mock galaxy catalogs in the future.Comment: 20 pages, 27 figures, 6 tables. Accepted to MNRA

Capturing the "Whole Tale" of Computational Research: Reproducibility in Computing Environments

We present an overview of the recently funded "Merging Science and Cyberinfrastructure Pathways: The Whole Tale" project (NSF award #1541450). Our approach has two nested goals: 1) deliver an environment that enables researchers to create a complete narrative of the research process including exposure of the data-to-publication lifecycle, and 2) systematically and persistently link research publications to their associated digital scholarly objects such as the data, code, and workflows. To enable this, Whole Tale will create an environment where researchers can collaborate on data, workspaces, and workflows and then publish them for future adoption or modification. Published data and applications will be consumed either directly by users using the Whole Tale environment or can be integrated into existing or future domain Science Gateways