281 research outputs found
GraffMatch: Global Matching of 3D Lines and Planes for Wide Baseline LiDAR Registration
Using geometric landmarks like lines and planes can increase navigation
accuracy and decrease map storage requirements compared to commonly-used LiDAR
point cloud maps. However, landmark-based registration for applications like
loop closure detection is challenging because a reliable initial guess is not
available. Global landmark matching has been investigated in the literature,
but these methods typically use ad hoc representations of 3D line and plane
landmarks that are not invariant to large viewpoint changes, resulting in
incorrect matches and high registration error. To address this issue, we adopt
the affine Grassmannian manifold to represent 3D lines and planes and prove
that the distance between two landmarks is invariant to rotation and
translation if a shift operation is performed before applying the Grassmannian
metric. This invariance property enables the use of our graph-based data
association framework for identifying landmark matches that can subsequently be
used for registration in the least-squares sense. Evaluated on a challenging
landmark matching and registration task using publicly-available LiDAR
datasets, our approach yields a 1.7x and 3.5x improvement in successful
registrations compared to methods that use viewpoint-dependent centroid and
"closest point" representations, respectively.Comment: accepted to RA-L; 8 pages. arXiv admin note: text overlap with
arXiv:2205.0855
Beyond Gauss: Image-Set Matching on the Riemannian Manifold of PDFs
State-of-the-art image-set matching techniques typically implicitly model
each image-set with a Gaussian distribution. Here, we propose to go beyond
these representations and model image-sets as probability distribution
functions (PDFs) using kernel density estimators. To compare and match
image-sets, we exploit Csiszar f-divergences, which bear strong connections to
the geodesic distance defined on the space of PDFs, i.e., the statistical
manifold. Furthermore, we introduce valid positive definite kernels on the
statistical manifolds, which let us make use of more powerful classification
schemes to match image-sets. Finally, we introduce a supervised dimensionality
reduction technique that learns a latent space where f-divergences reflect the
class labels of the data. Our experiments on diverse problems, such as
video-based face recognition and dynamic texture classification, evidence the
benefits of our approach over the state-of-the-art image-set matching methods
Buildings, spiders, and geometric Satake
Let G be a simple algebraic group. Labelled trivalent graphs called webs can
be used to product invariants in tensor products of minuscule representations.
For each web, we construct a configuration space of points in the affine
Grassmannian. Via the geometric Satake correspondence, we relate these
configuration spaces to the invariant vectors coming from webs. In the case G =
SL(3), non-elliptic webs yield a basis for the invariant spaces. The
non-elliptic condition, which is equivalent to the condition that the dual
diskoid of the web is CAT(0), is explained by the fact that affine buildings
are CAT(0).Comment: 49 pages; revised and to appear in Compositio Mathematic
Scattering Amplitudes and Toric Geometry
In this paper we provide a first attempt towards a toric geometric
interpretation of scattering amplitudes. In recent investigations it has indeed
been proposed that the all-loop integrand of planar N=4 SYM can be represented
in terms of well defined finite objects called on-shell diagrams drawn on
disks. Furthermore it has been shown that the physical information of on-shell
diagrams is encoded in the geometry of auxiliary algebraic varieties called the
totally non negative Grassmannians. In this new formulation the infinite
dimensional symmetry of the theory is manifest and many results, that are quite
tricky to obtain in terms of the standard Lagrangian formulation of the theory,
are instead manifest. In this paper, elaborating on previous results, we
provide another picture of the scattering amplitudes in terms of toric
geometry. In particular we describe in detail the toric varieties associated to
an on-shell diagram, how the singularities of the amplitudes are encoded in
some subspaces of the toric variety, and how this picture maps onto the
Grassmannian description. Eventually we discuss the action of cluster
transformations on the toric varieties. The hope is to provide an alternative
description of the scattering amplitudes that could contribute in the
developing of this very interesting field of research.Comment: 58 pages, 25 figures, typos corrected, a reference added, to be
published in JHE
Extrinsic Methods for Coding and Dictionary Learning on Grassmann Manifolds
Sparsity-based representations have recently led to notable results in
various visual recognition tasks. In a separate line of research, Riemannian
manifolds have been shown useful for dealing with features and models that do
not lie in Euclidean spaces. With the aim of building a bridge between the two
realms, we address the problem of sparse coding and dictionary learning over
the space of linear subspaces, which form Riemannian structures known as
Grassmann manifolds. To this end, we propose to embed Grassmann manifolds into
the space of symmetric matrices by an isometric mapping. This in turn enables
us to extend two sparse coding schemes to Grassmann manifolds. Furthermore, we
propose closed-form solutions for learning a Grassmann dictionary, atom by
atom. Lastly, to handle non-linearity in data, we extend the proposed Grassmann
sparse coding and dictionary learning algorithms through embedding into Hilbert
spaces.
Experiments on several classification tasks (gender recognition, gesture
classification, scene analysis, face recognition, action recognition and
dynamic texture classification) show that the proposed approaches achieve
considerable improvements in discrimination accuracy, in comparison to
state-of-the-art methods such as kernelized Affine Hull Method and
graph-embedding Grassmann discriminant analysis.Comment: Appearing in International Journal of Computer Visio
- …