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 $\sim 0.5$ 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

First-principles investigation of magnetocrystalline anisotropy oscillations in Co2_{2}FeAl/Ta heterostructures

We report first-principles investigations of magnetocrystalline anisotropy energy (MCAE) oscillations as a function of capping layer thickness in Heusler alloy Co\textsubscript{2}FeAl/Ta heterostructures. Substantial oscillation is observed in FeAl-interface structure. According to $k$-space and band-decomposed charge density analyses, this oscillation is mainly attributed to the Fermi-energy-vicinal quantum well states (QWS) which are confined between Co\textsubscript{2}FeAl/Ta interface and Ta/vacuum surface. The smaller oscillation magnitude in the Co-interface structure can be explained by the smooth potential transition at the interface. These findings clarify that MCAE in Co\textsubscript{2}FeAl/Ta is not a local property of the interface and that the quantum well effect plays a dominant role in MCAE oscillations of the heterostructures. This work presents the possibility of tuning MCAE by QWS in capping layers, and paves the way for artificially controlling magnetic anisotropy energy in magnetic tunnel junctions

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

Anatomy and giant enhancement of the perpendicular magnetic anisotropy of cobalt-graphene heterostructures

We report strongly enhanced perpendicular magnetic anisotropy (PMA) of Co films by graphene coating from both first-principles and experiments. Our calculations show that graphene can dramatically boost the surface anisotropy of Co films up to twice the value of its pristine counterpart and can extend the out-of-plane effective anisotropy up to unprecedented thickness of 25~\AA. These findings are supported by our experiments on graphene coating on Co films grown on Ir substrate. Furthermore, we report layer-resolved and orbital-hybridization-resolved anisotropy analysis which help understanding the physical mechanisms of PMA and more practically can help design structures with giant PMA. As an example, we propose super-exchange stabilized Co-graphene heterostructures with a robust out-of-plane constant effective PMA and linearly increasing interfacial anisotropy as a function of film thickness. These findings point towards possibilities to engineer graphene/ferromagnetic metal heterostructures with giant magnetic anisotropy more than 20 times larger compared to conventional multilayers, which constitutes a hallmark for future graphene and traditional spintronic technologies.Comment: 17 pages, 4 figure

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