146 research outputs found
Sorting photons by radial quantum number
The Laguerre-Gaussian (LG) modes constitute a complete basis set for
representing the transverse structure of a {paraxial} photon field in free
space. Earlier workers have shown how to construct a device for sorting a
photon according to its azimuthal LG mode index, which describes the orbital
angular momentum (OAM) carried by the field. In this paper we propose and
demonstrate a mode sorter based on the fractional Fourier transform (FRFT) to
efficiently decompose the optical field according to its radial profile. We
experimentally characterize the performance of our implementation by separating
individual radial modes as well as superposition states. The reported scheme
can, in principle, achieve unit efficiency and thus can be suitable for
applications that involve quantum states of light. This approach can be readily
combined with existing OAM mode sorters to provide a complete characterization
of the transverse profile of the optical field
MotionBEV: Attention-Aware Online LiDAR Moving Object Segmentation with Bird's Eye View based Appearance and Motion Features
Identifying moving objects is an essential capability for autonomous systems,
as it provides critical information for pose estimation, navigation, collision
avoidance, and static map construction. In this paper, we present MotionBEV, a
fast and accurate framework for LiDAR moving object segmentation, which
segments moving objects with appearance and motion features in the bird's eye
view (BEV) domain. Our approach converts 3D LiDAR scans into a 2D polar BEV
representation to improve computational efficiency. Specifically, we learn
appearance features with a simplified PointNet and compute motion features
through the height differences of consecutive frames of point clouds projected
onto vertical columns in the polar BEV coordinate system. We employ a
dual-branch network bridged by the Appearance-Motion Co-attention Module (AMCM)
to adaptively fuse the spatio-temporal information from appearance and motion
features. Our approach achieves state-of-the-art performance on the
SemanticKITTI-MOS benchmark. Furthermore, to demonstrate the practical
effectiveness of our method, we provide a LiDAR-MOS dataset recorded by a
solid-state LiDAR, which features non-repetitive scanning patterns and a small
field of view
High-dimensional quantum key distribution based on mutually partially unbiased bases
We propose a practical high-dimensional quantum key distribution protocol based on mutually partially unbiased bases utilizing transverse modes of light. In contrast to conventional protocols using mutually unbiased bases, our protocol uses Laguerre-Gaussian and Hermite-Gaussian modes of the same mode order as two mutually partially unbiased bases for encoding, which leads to a scheme free from mode-dependent diffraction in long-distance channels. Since only linear and passive optical elements are needed, our experimental implementation significantly simplifies qudit generation and state measurement. Since this protocol differs from conventional protocols using mutually unbiased bases, we provide a security analysis of our protocol
In-Domain GAN Inversion for Faithful Reconstruction and Editability
Generative Adversarial Networks (GANs) have significantly advanced image
synthesis through mapping randomly sampled latent codes to high-fidelity
synthesized images. However, applying well-trained GANs to real image editing
remains challenging. A common solution is to find an approximate latent code
that can adequately recover the input image to edit, which is also known as GAN
inversion. To invert a GAN model, prior works typically focus on reconstructing
the target image at the pixel level, yet few studies are conducted on whether
the inverted result can well support manipulation at the semantic level. This
work fills in this gap by proposing in-domain GAN inversion, which consists of
a domain-guided encoder and a domain-regularized optimizer, to regularize the
inverted code in the native latent space of the pre-trained GAN model. In this
way, we manage to sufficiently reuse the knowledge learned by GANs for image
reconstruction, facilitating a wide range of editing applications without any
retraining. We further make comprehensive analyses on the effects of the
encoder structure, the starting inversion point, as well as the inversion
parameter space, and observe the trade-off between the reconstruction quality
and the editing property. Such a trade-off sheds light on how a GAN model
represents an image with various semantics encoded in the learned latent
distribution. Code, models, and demo are available at the project page:
https://genforce.github.io/idinvert/
REC-MV: REconstructing 3D Dynamic Cloth from Monocular Videos
Reconstructing dynamic 3D garment surfaces with open boundaries from
monocular videos is an important problem as it provides a practical and
low-cost solution for clothes digitization. Recent neural rendering methods
achieve high-quality dynamic clothed human reconstruction results from
monocular video, but these methods cannot separate the garment surface from the
body. Moreover, despite existing garment reconstruction methods based on
feature curve representation demonstrating impressive results for garment
reconstruction from a single image, they struggle to generate temporally
consistent surfaces for the video input. To address the above limitations, in
this paper, we formulate this task as an optimization problem of 3D garment
feature curves and surface reconstruction from monocular video. We introduce a
novel approach, called REC-MV, to jointly optimize the explicit feature curves
and the implicit signed distance field (SDF) of the garments. Then the open
garment meshes can be extracted via garment template registration in the
canonical space. Experiments on multiple casually captured datasets show that
our approach outperforms existing methods and can produce high-quality dynamic
garment surfaces. The source code is available at
https://github.com/GAP-LAB-CUHK-SZ/REC-MV.Comment: CVPR2023; Project Page:https://lingtengqiu.github.io/2023/REC-MV
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