32,016 research outputs found
Convolutional Networks for Object Category and 3D Pose Estimation from 2D Images
Current CNN-based algorithms for recovering the 3D pose of an object in an
image assume knowledge about both the object category and its 2D localization
in the image. In this paper, we relax one of these constraints and propose to
solve the task of joint object category and 3D pose estimation from an image
assuming known 2D localization. We design a new architecture for this task
composed of a feature network that is shared between subtasks, an object
categorization network built on top of the feature network, and a collection of
category dependent pose regression networks. We also introduce suitable loss
functions and a training method for the new architecture. Experiments on the
challenging PASCAL3D+ dataset show state-of-the-art performance in the joint
categorization and pose estimation task. Moreover, our performance on the joint
task is comparable to the performance of state-of-the-art methods on the
simpler 3D pose estimation with known object category task
Dynamics of ultra-intense circularly polarized solitons under inhomogeneous plasmas
The dynamics of the ultra-intense circularly polarized solitons under
inhomogeneous plasmas are examined. The interaction is modeled by the Maxwell
and relativistic hydrodynamic equations and is solved with fully implicit
energy-conserving numerical scheme. It is shown that a propagating weak soliton
can be decreased and reflected by increasing plasma background, which is
consistent with the existing studies based on hypothesis of weak density
response. However it is found that ultra-intense soliton is well trapped and
kept still when encountering increasing background. Probably, this founding can
be applied for trapping and amplifying high-intensity laser-fields.Comment: 4 pages, 3 figures, submitted to Phys. Plasma
Recommended from our members
Highly Stable Luminous "snakes" from CsPbX3 Perovskite Nanocrystals Anchored on Amine-Coated Silica Nanowires
CsPbX3 (X = Cl, Br, and I) perovskite nanocrystals (NCs) are known for their exceptional optoelectronic properties, yet the material's instability toward polar solvents, heat, or UV irradiation greatly limits its further applications. Herein, an efficient in situ growing strategy has been developed to give highly stable perovskite NC composites (abbreviated CsPbX3@CA-SiO2) by anchoring CsPbX3 NCs onto silica nanowires (NWs), which effectively depresses the optical degradation of their photoluminescence (PL) and enhances stability. The preparation of surface-functionalized serpentine silica NWs is realized by a sol-gel process involving hydrolysis of a mixture of tetraethyl orthosilicate (TEOS), 3-aminopropyltriethoxysilane (APTES), and trimethoxy(octadecyl)silane (TMODS) in a water/oil emulsion. The serpentine NWs are formed via an anisotropic growth with lengths up to 8 μm. The free amino groups are employed as surface ligands for growing perovskite NCs, yielding distributed monodisperse NCs (∼8 nm) around the NW matrix. The emission wavelength is tunable by simple variation of the halide compositions (CsPbX3, X = Cl, Br, or I), and the composites demonstrate a high photoluminescence quantum yield (PLQY 32-69%). Additionally, we have demonstrated the composites CsPbX3@CA-SiO2 can be self-woven to form a porous 3D hierarchical NWs membrane, giving rise to a superhydrophobic surface with hierarchical micro/nano structural features. The resulting composites exhibit high stability toward water, heat, and UV irradiation. This work elucidates an effective strategy to incorporate perovskite nanocrystals onto functional matrices as multifunctional stable light sources
Wood-Inspired Morphologically Tunable Aligned Hydrogel for High-Performance Flexible All-Solid-State Supercapacitors
Oriented microstructures are widely found in various biological systems for multiple functions. Such anisotropic structures provide low tortuosity and sufficient surface area, desirable for the design of high-performance energy storage devices. Despite significant efforts to develop supercapacitors with aligned morphology, challenges remain due to the predefined pore sizes, limited mechanical flexibility, and low mass loading. Herein, a wood-inspired flexible all-solid-state hydrogel supercapacitor is demonstrated by morphologically tuning the aligned hydrogel matrix toward high electrode-materials loading and high areal capacitance. The highly aligned matrix exhibits broad morphological tunability (47–12 µm), mechanical flexibility (0°–180° bending), and uniform polypyrrole loading up to 7 mm thick matrix. After being assembled into a solid-state supercapacitor, the areal capacitance reaches 831 mF cm−2 for the 12 µm matrix, which is 259% times of the 47 µm matrix and 403% times of nonaligned matrix. The supercapacitor also exhibits a high energy density of 73.8 µWh cm−2, power density of 4960 µW cm−2, capacitance retention of 86.5% after 1000 cycles, and bending stability of 95% after 5000 cycles. The principle to structurally design the oriented matrices for high electrode material loading opens up the possibility for advanced energy storage applications
Suppressing longitudinal double-layer oscillations by using elliptically polarized laser pulses in the hole-boring radiation pressure acceleration regime
It is shown that well collimated mono-energetic ion beams with a large
particle number can be generated in the hole-boring radiation pressure
acceleration regime by using an elliptically polarized laser pulse with
appropriate theoretically determined laser polarization ratio. Due to the
effect, the double-layer charge separation region is
imbued with hot electrons that prevent ion pileup, thus suppressing the
double-layer oscillations. The proposed mechanism is well confirmed by
Particle-in-Cell simulations, and after suppressing the longitudinal
double-layer oscillations, the ion beams driven by the elliptically polarized
lasers own much better energy spectrum than those by circularly polarized
lasers.Comment: 6 pages, 5 figures, Phys. Plasmas (2013) accepte
Dense feature correspondence for video-based endoscope three-dimensional motion tracking
This paper presents an improved video-based endoscope tracking approach on the basis of dense feature correspondence. Currently video-based methods often fail to track the endoscope motion due to low-quality endoscopic video images. To address such failure, we use image texture information to boost the tracking performance. A local image descriptor - DAISY is introduced to efficiently detect dense texture or feature information from endoscopic images. After these dense feature correspondence, we compute relative motion parameters between the previous and current endoscopic images in terms of epipolar geometric analysis. By initializing with the relative motion information, we perform 2-D/3-D or video-volume registration and determine the current endoscope pose information with six degrees of freedom (6DoF) position and orientation parameters. We evaluate our method on clinical datasets. Experimental results demonstrate that our proposed method outperforms state-of-the-art approaches. The tracking error was significantly reduced from 7.77 mm to 4.78 mm. © 2014 IEEE
- …