44,244 research outputs found
GraphMatch: Efficient Large-Scale Graph Construction for Structure from Motion
We present GraphMatch, an approximate yet efficient method for building the
matching graph for large-scale structure-from-motion (SfM) pipelines. Unlike
modern SfM pipelines that use vocabulary (Voc.) trees to quickly build the
matching graph and avoid a costly brute-force search of matching image pairs,
GraphMatch does not require an expensive offline pre-processing phase to
construct a Voc. tree. Instead, GraphMatch leverages two priors that can
predict which image pairs are likely to match, thereby making the matching
process for SfM much more efficient. The first is a score computed from the
distance between the Fisher vectors of any two images. The second prior is
based on the graph distance between vertices in the underlying matching graph.
GraphMatch combines these two priors into an iterative "sample-and-propagate"
scheme similar to the PatchMatch algorithm. Its sampling stage uses Fisher
similarity priors to guide the search for matching image pairs, while its
propagation stage explores neighbors of matched pairs to find new ones with a
high image similarity score. Our experiments show that GraphMatch finds the
most image pairs as compared to competing, approximate methods while at the
same time being the most efficient.Comment: Published at IEEE 3DV 201
Semantic Visual Localization
Robust visual localization under a wide range of viewing conditions is a
fundamental problem in computer vision. Handling the difficult cases of this
problem is not only very challenging but also of high practical relevance,
e.g., in the context of life-long localization for augmented reality or
autonomous robots. In this paper, we propose a novel approach based on a joint
3D geometric and semantic understanding of the world, enabling it to succeed
under conditions where previous approaches failed. Our method leverages a novel
generative model for descriptor learning, trained on semantic scene completion
as an auxiliary task. The resulting 3D descriptors are robust to missing
observations by encoding high-level 3D geometric and semantic information.
Experiments on several challenging large-scale localization datasets
demonstrate reliable localization under extreme viewpoint, illumination, and
geometry changes
BeSpaceD: Towards a Tool Framework and Methodology for the Specification and Verification of Spatial Behavior of Distributed Software Component Systems
In this report, we present work towards a framework for modeling and checking
behavior of spatially distributed component systems. Design goals of our
framework are the ability to model spatial behavior in a component oriented,
simple and intuitive way, the possibility to automatically analyse and verify
systems and integration possibilities with other modeling and verification
tools. We present examples and the verification steps necessary to prove
properties such as range coverage or the absence of collisions between
components and technical details
Linear Global Translation Estimation with Feature Tracks
This paper derives a novel linear position constraint for cameras seeing a
common scene point, which leads to a direct linear method for global camera
translation estimation. Unlike previous solutions, this method deals with
collinear camera motion and weak image association at the same time. The final
linear formulation does not involve the coordinates of scene points, which
makes it efficient even for large scale data. We solve the linear equation
based on norm, which makes our system more robust to outliers in
essential matrices and feature correspondences. We experiment this method on
both sequentially captured images and unordered Internet images. The
experiments demonstrate its strength in robustness, accuracy, and efficiency.Comment: Changes: 1. Adopt BMVC2015 style; 2. Combine sections 3 and 5; 3.
Move "Evaluation on synthetic data" out to supplementary file; 4. Divide
subsection "Evaluation on general data" to subsections "Experiment on
sequential data" and "Experiment on unordered Internet data"; 5. Change Fig.
1 and Fig.8; 6. Move Fig. 6 and Fig. 7 to supplementary file; 7 Change some
symbols; 8. Correct some typo
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
InLoc: Indoor Visual Localization with Dense Matching and View Synthesis
We seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph
with respect to a large indoor 3D map. The contributions of this work are
three-fold. First, we develop a new large-scale visual localization method
targeted for indoor environments. The method proceeds along three steps: (i)
efficient retrieval of candidate poses that ensures scalability to large-scale
environments, (ii) pose estimation using dense matching rather than local
features to deal with textureless indoor scenes, and (iii) pose verification by
virtual view synthesis to cope with significant changes in viewpoint, scene
layout, and occluders. Second, we collect a new dataset with reference 6DoF
poses for large-scale indoor localization. Query photographs are captured by
mobile phones at a different time than the reference 3D map, thus presenting a
realistic indoor localization scenario. Third, we demonstrate that our method
significantly outperforms current state-of-the-art indoor localization
approaches on this new challenging data
Hypergraph Modelling for Geometric Model Fitting
In this paper, we propose a novel hypergraph based method (called HF) to fit
and segment multi-structural data. The proposed HF formulates the geometric
model fitting problem as a hypergraph partition problem based on a novel
hypergraph model. In the hypergraph model, vertices represent data points and
hyperedges denote model hypotheses. The hypergraph, with large and
"data-determined" degrees of hyperedges, can express the complex relationships
between model hypotheses and data points. In addition, we develop a robust
hypergraph partition algorithm to detect sub-hypergraphs for model fitting. HF
can effectively and efficiently estimate the number of, and the parameters of,
model instances in multi-structural data heavily corrupted with outliers
simultaneously. Experimental results show the advantages of the proposed method
over previous methods on both synthetic data and real images.Comment: Pattern Recognition, 201
Collaborative Verification-Driven Engineering of Hybrid Systems
Hybrid systems with both discrete and continuous dynamics are an important
model for real-world cyber-physical systems. The key challenge is to ensure
their correct functioning w.r.t. safety requirements. Promising techniques to
ensure safety seem to be model-driven engineering to develop hybrid systems in
a well-defined and traceable manner, and formal verification to prove their
correctness. Their combination forms the vision of verification-driven
engineering. Often, hybrid systems are rather complex in that they require
expertise from many domains (e.g., robotics, control systems, computer science,
software engineering, and mechanical engineering). Moreover, despite the
remarkable progress in automating formal verification of hybrid systems, the
construction of proofs of complex systems often requires nontrivial human
guidance, since hybrid systems verification tools solve undecidable problems.
It is, thus, not uncommon for development and verification teams to consist of
many players with diverse expertise. This paper introduces a
verification-driven engineering toolset that extends our previous work on
hybrid and arithmetic verification with tools for (i) graphical (UML) and
textual modeling of hybrid systems, (ii) exchanging and comparing models and
proofs, and (iii) managing verification tasks. This toolset makes it easier to
tackle large-scale verification tasks
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