268 research outputs found
Electro-Optical Manipulation Based on Dielectric Nanoparticles
The ability to dynamically modulate plasmon resonances or Mie resonances is crucial for practical application. Electrical tuning as one of the most efficiently active tuning methods has high switching speed and large modulation depth. Silicon as a typical high refractive index dielectric material can generate strong Mie resonances, which have shown comparable performances with plasmonic nanostructures in spectral tailoring and phase modulation. However, it is still unclear whether the optical response of single silicon nanoantenna can be electrically controlled effectively. In this chapter, we introduce two types of optoelectronic devices based on Mie resonances in silicon nanoantennas. First, we observe obvious blueshift and intensity attenuation of the plasmon-dielectric hybrid resonant peaks when applying bias voltages. Second, photoluminescence (PL) enhancement and modulation are achieved together in the WS2-Mie resonator hybrid system
Cascade Residual Learning: A Two-stage Convolutional Neural Network for Stereo Matching
Leveraging on the recent developments in convolutional neural networks
(CNNs), matching dense correspondence from a stereo pair has been cast as a
learning problem, with performance exceeding traditional approaches. However,
it remains challenging to generate high-quality disparities for the inherently
ill-posed regions. To tackle this problem, we propose a novel cascade CNN
architecture composing of two stages. The first stage advances the recently
proposed DispNet by equipping it with extra up-convolution modules, leading to
disparity images with more details. The second stage explicitly rectifies the
disparity initialized by the first stage; it couples with the first-stage and
generates residual signals across multiple scales. The summation of the outputs
from the two stages gives the final disparity. As opposed to directly learning
the disparity at the second stage, we show that residual learning provides more
effective refinement. Moreover, it also benefits the training of the overall
cascade network. Experimentation shows that our cascade residual learning
scheme provides state-of-the-art performance for matching stereo
correspondence. By the time of the submission of this paper, our method ranks
first in the KITTI 2015 stereo benchmark, surpassing the prior works by a
noteworthy margin.Comment: Accepted at ICCVW 2017. The first two authors contributed equally to
this pape
Neural Video Compression with Diverse Contexts
For any video codecs, the coding efficiency highly relies on whether the
current signal to be encoded can find the relevant contexts from the previous
reconstructed signals. Traditional codec has verified more contexts bring
substantial coding gain, but in a time-consuming manner. However, for the
emerging neural video codec (NVC), its contexts are still limited, leading to
low compression ratio. To boost NVC, this paper proposes increasing the context
diversity in both temporal and spatial dimensions. First, we guide the model to
learn hierarchical quality patterns across frames, which enriches long-term and
yet high-quality temporal contexts. Furthermore, to tap the potential of
optical flow-based coding framework, we introduce a group-based offset
diversity where the cross-group interaction is proposed for better context
mining. In addition, this paper also adopts a quadtree-based partition to
increase spatial context diversity when encoding the latent representation in
parallel. Experiments show that our codec obtains 23.5% bitrate saving over
previous SOTA NVC. Better yet, our codec has surpassed the under-developing
next generation traditional codec/ECM in both RGB and YUV420 colorspaces, in
terms of PSNR. The codes are at https://github.com/microsoft/DCVC.Comment: Accepted by CVPR 2023. Codes are at https://github.com/microsoft/DCV
Inertia of partial transpose of positive semidefinite matrices
We show that the partial transpose of positive semidefinite
matrices do not have inertia (4,1,4) and (3,2,4). It solves an open problem in
"LINEAR AND MULTILINEAR ALGEBRA, Changchun Feng et al, 2022". We apply our
results to construct some inertia, as well as present the list of all possible
inertia of partial transpose of positive semidefinite matrices.Comment: 20 pages, comments are welcom
Accurate Single Stage Detector Using Recurrent Rolling Convolution
Most of the recent successful methods in accurate object detection and
localization used some variants of R-CNN style two stage Convolutional Neural
Networks (CNN) where plausible regions were proposed in the first stage then
followed by a second stage for decision refinement. Despite the simplicity of
training and the efficiency in deployment, the single stage detection methods
have not been as competitive when evaluated in benchmarks consider mAP for high
IoU thresholds. In this paper, we proposed a novel single stage end-to-end
trainable object detection network to overcome this limitation. We achieved
this by introducing Recurrent Rolling Convolution (RRC) architecture over
multi-scale feature maps to construct object classifiers and bounding box
regressors which are "deep in context". We evaluated our method in the
challenging KITTI dataset which measures methods under IoU threshold of 0.7. We
showed that with RRC, a single reduced VGG-16 based model already significantly
outperformed all the previously published results. At the time this paper was
written our models ranked the first in KITTI car detection (the hard level),
the first in cyclist detection and the second in pedestrian detection. These
results were not reached by the previous single stage methods. The code is
publicly available.Comment: CVPR 201
An Extrinsic Calibration Method of a 3D-LiDAR and a Pose Sensor for Autonomous Driving
Accurate and reliable sensor calibration is critical for fusing LiDAR and
inertial measurements in autonomous driving. This paper proposes a novel
three-stage extrinsic calibration method of a 3D-LiDAR and a pose sensor for
autonomous driving. The first stage can quickly calibrate the extrinsic
parameters between the sensors through point cloud surface features so that the
extrinsic can be narrowed from a large initial error to a small error range in
little time. The second stage can further calibrate the extrinsic parameters
based on LiDAR-mapping space occupancy while removing motion distortion. In the
final stage, the z-axis errors caused by the plane motion of the autonomous
vehicle are corrected, and an accurate extrinsic parameter is finally obtained.
Specifically, This method utilizes the natural characteristics of road scenes,
making it independent and easy to apply in large-scale conditions. Experimental
results on real-world data sets demonstrate the reliability and accuracy of our
method. The codes are open-sourced on the Github website. To the best of our
knowledge, this is the first open-source code specifically designed for
autonomous driving to calibrate LiDAR and pose-sensor extrinsic parameters. The
code link is https://github.com/OpenCalib/LiDAR2INS.Comment: 7 pages, 12 figure
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