Lifting GIS Maps into Strong Geometric Context for Scene Understanding

Contextual information can have a substantial impact on the performance of visual tasks such as semantic segmentation, object detection, and geometric estimation. Data stored in Geographic Information Systems (GIS) offers a rich source of contextual information that has been largely untapped by computer vision. We propose to leverage such information for scene understanding by combining GIS resources with large sets of unorganized photographs using Structure from Motion (SfM) techniques. We present a pipeline to quickly generate strong 3D geometric priors from 2D GIS data using SfM models aligned with minimal user input. Given an image resectioned against this model, we generate robust predictions of depth, surface normals, and semantic labels. We show that the precision of the predicted geometry is substantially more accurate other single-image depth estimation methods. We then demonstrate the utility of these contextual constraints for re-scoring pedestrian detections, and use these GIS contextual features alongside object detection score maps to improve a CRF-based semantic segmentation framework, boosting accuracy over baseline models

Equivariant Oka theory: survey of recent progress.

We survey recent work, published since 2015, on equivariant Oka theory. The main results described in the survey are as follows. Homotopy principles for equivariant isomorphisms of Stein manifolds on which a reductive complex Lie group G acts. Applications to the linearisation problem. A parametric Oka principle for sections of a bundle E of homogeneous spaces for a group bundle , all over a reduced Stein space X with compatible actions of a reductive complex group on E, , and X. Application to the classification of generalised principal bundles with a group action. Finally, an equivariant version of Gromov's Oka principle based on a notion of a G-manifold being G-Oka

Equivariant Oka theory: survey of recent progress.

We survey recent work, published since 2015, on equivariant Oka theory. The main results described in the survey are as follows. Homotopy principles for equivariant isomorphisms of Stein manifolds on which a reductive complex Lie group G acts. Applications to the linearisation problem. A parametric Oka principle for sections of a bundle E of homogeneous spaces for a group bundle , all over a reduced Stein space X with compatible actions of a reductive complex group on E, , and X. Application to the classification of generalised principal bundles with a group action. Finally, an equivariant version of Gromov's Oka principle based on a notion of a G-manifold being G-Oka

Radiography of the Past - Three Dimensional, Virtual Reconstruction of a Roman Town in Lusitania

The European project, "RADIO-PAST" was launched in 2009 within the Marie Curie framework "Industry-Academia Partnerships and Pathways". The project aims to join resources and different skills to tackle each possible aspect connected with "non-destructive" approaches to understand and reconstruct complex archaeological sites. The consortium of 7 partners has chosen an "open laboratory for research and experimentation" in and around the abandoned Roman site of Ammaia in central Portugal, but some research activities are carried out by the partner institutions in different areas of the Mediterranean and continental Europe. This paper describes the various methods and procedures which were used to undertake the three dimensional reconstruction of this Roman urban site in Lusitania.7reasons Medien GmbH, 1200 Vienna, Bäuerlegasse 3-4, Austria, Department of Archaeology, Ghent University, 9000 Gent, Sint-Pietersnieuwstraat 35, University of Évora CIDEHUS, 7002-554 Évora, Palácio do Vimioso Apartado 94, Portugal, Department of Humanistics, University of Cassino, 03043 Cassino (FR), Via Marconi 10, Italy

Cluster-Wise Ratio Tests for Fast Camera Localization

Feature point matching for camera localization suffers from scalability problems. Even when feature descriptors associated with 3D scene points are locally unique, as coverage grows, similar or repeated features become increasingly common. As a result, the standard distance ratio-test used to identify reliable image feature points is overly restrictive and rejects many good candidate matches. We propose a simple coarse-to-fine strategy that uses conservative approximations to robust local ratio-tests that can be computed efficiently using global approximate k-nearest neighbor search. We treat these forward matches as votes in camera pose space and use them to prioritize back-matching within candidate camera pose clusters, exploiting feature co-visibility captured by clustering the 3D model camera pose graph. This approach achieves state-of-the-art camera localization results on a variety of popular benchmarks, outperforming several methods that use more complicated data structures and that make more restrictive assumptions on camera pose. We also carry out diagnostic analyses on a difficult test dataset containing globally repetitive structure that suggest our approach successfully adapts to the challenges of large-scale image localization

On the equivalence between Lurie's model and the dendroidal model for infinity-operads

© 2016 Elsevier Inc.We compare two approaches to the homotopy theory of ∞-operads. One of them, the theory of dendroidal sets, is based on an extension of the theory of simplicial sets and ∞-categories which replaces simplices by trees. The other is based on a certain homotopy theory of marked simplicial sets over the nerve of Segal's category Γ. In this paper we prove that for operads without constants these two theories are equivalent, in the precise sense of the existence of a zig-zag of Quillen equivalences between the respective model categories