A factorization approach to inertial affine structure from motion

We consider the problem of reconstructing a 3-D scene from a moving camera with high frame rate using the affine projection model. This problem is traditionally known as Affine Structure from Motion (Affine SfM), and can be solved using an elegant low-rank factorization formulation. In this paper, we assume that an accelerometer and gyro are rigidly mounted with the camera, so that synchronized linear acceleration and angular velocity measurements are available together with the image measurements. We extend the standard Affine SfM algorithm to integrate these measurements through the use of image derivatives

Sedimentological characterization of Antarctic moraines using UAVs and Structure-from-Motion photogrammetry

In glacial environments particle-size analysis of moraines provides insights into clast origin, transport history, depositional mechanism and processes of reworking. Traditional methods for grain-size classification are labour-intensive, physically intrusive and are limited to patch-scale (1m2) observation. We develop emerging, high-resolution ground- and unmanned aerial vehicle-based ‘Structure-from-Motion’ (UAV-SfM) photogrammetry to recover grain-size information across an moraine surface in the Heritage Range, Antarctica. SfM data products were benchmarked against equivalent datasets acquired using terrestrial laser scanning, and were found to be accurate to within 1.7 and 50mm for patch- and site-scale modelling, respectively. Grain-size distributions were obtained through digital grain classification, or ‘photo-sieving’, of patch-scale SfM orthoimagery. Photo-sieved distributions were accurate to <2mm compared to control distributions derived from dry sieving. A relationship between patch-scale median grain size and the standard deviation of local surface elevations was applied to a site-scale UAV-SfM model to facilitate upscaling and the production of a spatially continuous map of the median grain size across a 0.3 km2 area of moraine. This highly automated workflow for site scale sedimentological characterization eliminates much of the subjectivity associated with traditional methods and forms a sound basis for subsequent glaciological process interpretation and analysis

GSLAM: Initialization-robust Monocular Visual SLAM via Global Structure-from-Motion

Many monocular visual SLAM algorithms are derived from incremental structure-from-motion (SfM) methods. This work proposes a novel monocular SLAM method which integrates recent advances made in global SfM. In particular, we present two main contributions to visual SLAM. First, we solve the visual odometry problem by a novel rank-1 matrix factorization technique which is more robust to the errors in map initialization. Second, we adopt a recent global SfM method for the pose-graph optimization, which leads to a multi-stage linear formulation and enables L1 optimization for better robustness to false loops. The combination of these two approaches generates more robust reconstruction and is significantly faster (4X) than recent state-of-the-art SLAM systems. We also present a new dataset recorded with ground truth camera motion in a Vicon motion capture room, and compare our method to prior systems on it and established benchmark datasets.Comment: 3DV 2017 Project Page: https://frobelbest.github.io/gsla

Suitability of ground-based SfM-MVS for monitoring glacial and periglacial processes

Photo-based surface reconstruction is rapidly emerging as an alternative survey technique to lidar (light detection and ranging) in many fields of geoscience fostered by the recent development of computer vision algorithms such as structure from motion (SfM) and dense image matching such as multi-view stereo (MVS). The objectives of this work are to test the suitability of the ground-based SfM-MVS approach for calculating the geodetic mass balance of a 2.1km2 glacier and for detecting the surface displacement of a neighbouring active rock glacier located in the eastern Italian Alps. The photos were acquired in 2013 and 2014 using a digital consumer-grade camera during single-day field surveys. Airborne laser scanning (ALS, otherwise known as airborne lidar) data were used as benchmarks to estimate the accuracy of the photogrammetric digital elevation models (DEMs) and the reliability of the method. The SfM-MVS approach enabled the reconstruction of high-quality DEMs, which provided estimates of glacial and periglacial processes similar to those achievable using ALS. In stable bedrock areas outside the glacier, the mean and the standard deviation of the elevation difference between the SfM-MVS DEM and the ALS DEM was-0.42 \ub1 1.72 and 0.03 \ub1 0.74 m in 2013 and 2014, respectively. The overall pattern of elevation loss and gain on the glacier were similar with both methods, ranging between-5.53 and + 3.48 m. In the rock glacier area, the elevation difference between the SfM-MVS DEM and the ALS DEM was 0.02 \ub1 0.17 m. The SfM-MVS was able to reproduce the patterns and the magnitudes of displacement of the rock glacier observed by the ALS, ranging between 0.00 and 0.48 m per year. The use of natural targets as ground control points, the occurrence of shadowed and low-contrast areas, and in particular the suboptimal camera network geometry imposed by the morphology of the study area were the main factors affecting the accuracy of photogrammetric DEMs negatively. Technical improvements such as using an aerial platform and/or placing artificial targets could significantly improve the results but run the risk of being more demanding in terms of costs and logistics

Are multiphase competition & order-by-disorder the keys to understanding Yb2Ti2O7?

If magnetic frustration is most commonly known for undermining long-range order, as famously illustrated by spin liquids, the ability of matter to develop new collective mechanisms in order to fight frustration is no less fascinating, providing an avenue for the exploration and discovery of unconventional properties of matter. Here we study an ideal minimal model of such mechanisms which, incidentally, pertains to the perplexing quantum spin ice candidate Yb2Ti2O7. Specifically, we explain how thermal and quantum fluctuations, optimized by order-by-disorder selection, conspire to expand the stability region of an accidentally degenerate continuous symmetry U(1) manifold against the classical splayed ferromagnetic ground state that is displayed by the sister compound Yb2Sn2O7. The resulting competition gives rise to multiple phase transitions, in striking similitude with recent experiments on Yb2Ti2O7 [Lhotel et al., Phys. Rev. B 89 224419 (2014)]. Considering the effective Hamiltonian determined for Yb2Ti2O7, we provide, by combining a gamut of numerical techniques, compelling evidence that such multiphase competition is the long-sought missing key to understanding the intrinsic properties of this material. As a corollary, our work offers a pertinent illustration of the influence of chemical pressure in rare-earth pyrochlores.Comment: 9 page

Learning Single-Image Depth from Videos using Quality Assessment Networks

Depth estimation from a single image in the wild remains a challenging problem. One main obstacle is the lack of high-quality training data for images in the wild. In this paper we propose a method to automatically generate such data through Structure-from-Motion (SfM) on Internet videos. The core of this method is a Quality Assessment Network that identifies high-quality reconstructions obtained from SfM. Using this method, we collect single-view depth training data from a large number of YouTube videos and construct a new dataset called YouTube3D. Experiments show that YouTube3D is useful in training depth estimation networks and advances the state of the art of single-view depth estimation in the wild