447 research outputs found
Face Recognition from Sequential Sparse 3D Data via Deep Registration
Previous works have shown that face recognition with high accurate 3D data is
more reliable and insensitive to pose and illumination variations. Recently,
low-cost and portable 3D acquisition techniques like ToF(Time of Flight) and
DoE based structured light systems enable us to access 3D data easily, e.g.,
via a mobile phone. However, such devices only provide sparse(limited speckles
in structured light system) and noisy 3D data which can not support face
recognition directly. In this paper, we aim at achieving high-performance face
recognition for devices equipped with such modules which is very meaningful in
practice as such devices will be very popular. We propose a framework to
perform face recognition by fusing a sequence of low-quality 3D data. As 3D
data are sparse and noisy which can not be well handled by conventional methods
like the ICP algorithm, we design a PointNet-like Deep Registration
Network(DRNet) which works with ordered 3D point coordinates while preserving
the ability of mining local structures via convolution. Meanwhile we develop a
novel loss function to optimize our DRNet based on the quaternion expression
which obviously outperforms other widely used functions. For face recognition,
we design a deep convolutional network which takes the fused 3D depth-map as
input based on AMSoftmax model. Experiments show that our DRNet can achieve
rotation error 0.95{\deg} and translation error 0.28mm for registration. The
face recognition on fused data also achieves rank-1 accuracy 99.2% , FAR-0.001
97.5% on Bosphorus dataset which is comparable with state-of-the-art
high-quality data based recognition performance.Comment: To be appeared in ICB201
Inducing and Optimizing Magnetism in Graphene Nanomesh
Using first-principles calculations, we explore the electronic and magnetic
properties of graphene nanomesh (GNM), a regular network of large vacancies,
produced either by lithography or nanoimprint. When removing an equal number of
A and B sites of the graphene bipartite lattice, the nanomesh made mostly of
zigzag (armchair) type edges exhibit antiferromagnetic (spin unpolarized)
states. In contrast, in situation of sublattice symmetry breaking, stable
ferri(o)magnetic states are obtained. For hydrogen-passivated nanomesh, the
formation energy is dramatically decreased, and ground state is found to
strongly depend on the vacancies shape and size. For triangular shaped holes,
the obtained net magnetic moments increase with the number difference of
removed A and B sites in agreement with Lieb's theorem for even A+B. For odd
A+B triangular meshes and all cases of non-triangular nanomeshes including the
one with even A+B, Lieb's theorem does not hold anymore which can be partially
attributed to introduction of armchair edges. In addition, large triangular
shaped GNM could be as robust as non-triangular GNMs, providing possible
solution to overcome one of crucial challenges for the sp-magnetism. Finally,
significant exchange splitting values as large as eV can be obtained
for highly asymmetric structures evidencing the potential of GNM for room
temperature carbon based spintronics. These results demonstrate that a turn
from 0-dimensional graphene nanoflakes throughout 1-dimensional graphene
nanoribbons with zigzag edges to GNM breaks localization of unpaired electrons
and provides deviation from the rules based on Lieb's theorem. Such
delocalization of the electrons leads the switch of the ground state of system
from antiferromagnetic narrow gap insulator discussed for graphene nanoribons
to ferromagnetic or nonmagnetic metal.Comment: 7 pages, 5 figures, 1 tabl
Building3D: An Urban-Scale Dataset and Benchmarks for Learning Roof Structures from Point Clouds
Urban modeling from LiDAR point clouds is an important topic in computer
vision, computer graphics, photogrammetry and remote sensing. 3D city models
have found a wide range of applications in smart cities, autonomous navigation,
urban planning and mapping etc. However, existing datasets for 3D modeling
mainly focus on common objects such as furniture or cars. Lack of building
datasets has become a major obstacle for applying deep learning technology to
specific domains such as urban modeling. In this paper, we present a
urban-scale dataset consisting of more than 160 thousands buildings along with
corresponding point clouds, mesh and wire-frame models, covering 16 cities in
Estonia about 998 Km2. We extensively evaluate performance of state-of-the-art
algorithms including handcrafted and deep feature based methods. Experimental
results indicate that Building3D has challenges of high intra-class variance,
data imbalance and large-scale noises. The Building3D is the first and largest
urban-scale building modeling benchmark, allowing a comparison of supervised
and self-supervised learning methods. We believe that our Building3D will
facilitate future research on urban modeling, aerial path planning, mesh
simplification, and semantic/part segmentation etc
Thermal deformation analysis of high density phase change optical disks
Master'sMASTER OF ENGINEERIN
Tuning the Dzyaloshinskii-Moriya Interaction in Pt/Co/MgO heterostructures through MgO thickness
The interfacial Dzyaloshinskii-Moriya interaction (DMI) in the
ferromagnetic/heavy metal ultra-thin film structures , has attracted a lot of
attention thanks to its capability to stabilize Neel-type domain walls (DWs)
and magnetic skyrmions for the realization of non-volatile memory and logic
devices. In this study, we demonstrate that magnetic properties in
perpendicularly magnetized Ta/Pt/Co/MgO/Pt heterostructures, such as
magnetization and DMI, can be significantly influenced through both the MgO and
the Co ultrathin film thickness. By using a field-driven creep regime domain
expansion technique, we find that non-monotonic tendencies of DMI field appear
when changing the thickness of MgO and the MgO thickness corresponding to the
largest DMI field varies as a function of the Co thicknesses. We interpret this
efficient control of DMI as subtle changes of both Pt/Co and Co/MgO interfaces,
which provide a method to investigate ultra-thin structures design to achieve
skyrmion electronics.Comment: 18 pages, 11 figure
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