114 research outputs found
Heteronuclear magnetisms with ultracold spinor bosonic gases in optical lattices
Motivated by recent realizations of spin-1 NaRb mixtures in the experiments,
here we investigate heteronuclear magnetism in the Mott-insulating regime.
Different from the identical mixtures where the boson (fermion) statistics only
admits even (odd) parity states from angular momentum composition, for
heteronuclear atoms in principle all angular momentum states are allowed, which
can give rise to new magnetic phases. Various magnetic phases can be developed
over these degenerate spaces, however, the concrete symmetry breaking phases
depend not only on the degree of degeneracy, but also the competitions from
many-body interactions. We unveil these rich phases using the bosonic dynamical
mean-field theory approach. These phases are characterized by various orders,
including spontaneous magnetization order, spin magnitude order, singlet
pairing order and nematic order, which may coexist, especially in the regime
with odd parity. Finally we address the possible parameter regimes for
observing these spin-ordered Mott phases.Comment: 6 pages, 4 figures, with supplementary materials (8 pages
Estimating 6D Pose From Localizing Designated Surface Keypoints
In this paper, we present an accurate yet effective solution for 6D pose
estimation from an RGB image. The core of our approach is that we first
designate a set of surface points on target object model as keypoints and then
train a keypoint detector (KPD) to localize them. Finally a PnP algorithm can
recover the 6D pose according to the 2D-3D relationship of keypoints. Different
from recent state-of-the-art CNN-based approaches that rely on a time-consuming
post-processing procedure, our method can achieve competitive accuracy without
any refinement after pose prediction. Meanwhile, we obtain a 30% relative
improvement in terms of ADD accuracy among methods without using refinement.
Moreover, we succeed in handling heavy occlusion by selecting the most
confident keypoints to recover the 6D pose. For the sake of reproducibility, we
will make our code and models publicly available soon
Robotic Surgery Remote Mentoring via AR with 3D Scene Streaming and Hand Interaction
With the growing popularity of robotic surgery, education becomes
increasingly important and urgently needed for the sake of patient safety.
However, experienced surgeons have limited accessibility due to their busy
clinical schedule or working in a distant city, thus can hardly provide
sufficient education resources for novices. Remote mentoring, as an effective
way, can help solve this problem, but traditional methods are limited to plain
text, audio, or 2D video, which are not intuitive nor vivid. Augmented reality
(AR), a thriving technique being widely used for various education scenarios,
is promising to offer new possibilities of visual experience and interactive
teaching. In this paper, we propose a novel AR-based robotic surgery remote
mentoring system with efficient 3D scene visualization and natural 3D hand
interaction. Using a head-mounted display (i.e., HoloLens), the mentor can
remotely monitor the procedure streamed from the trainee's operation side. The
mentor can also provide feedback directly with hand gestures, which is in-turn
transmitted to the trainee and viewed in surgical console as guidance. We
comprehensively validate the system on both real surgery stereo videos and
ex-vivo scenarios of common robotic training tasks (i.e., peg-transfer and
suturing). Promising results are demonstrated regarding the fidelity of
streamed scene visualization, the accuracy of feedback with hand interaction,
and the low-latency of each component in the entire remote mentoring system.
This work showcases the feasibility of leveraging AR technology for reliable,
flexible and low-cost solutions to robotic surgical education, and holds great
potential for clinical applications
Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey and Experimental Study
3D semantic segmentation is a fundamental task for robotic and autonomous
driving applications. Recent works have been focused on using deep learning
techniques, whereas developing fine-annotated 3D LiDAR datasets is extremely
labor intensive and requires professional skills. The performance limitation
caused by insufficient datasets is called data hunger problem. This research
provides a comprehensive survey and experimental study on the question: are we
hungry for 3D LiDAR data for semantic segmentation? The studies are conducted
at three levels. First, a broad review to the main 3D LiDAR datasets is
conducted, followed by a statistical analysis on three representative datasets
to gain an in-depth view on the datasets' size and diversity, which are the
critical factors in learning deep models. Second, a systematic review to the
state-of-the-art 3D semantic segmentation is conducted, followed by experiments
and cross examinations of three representative deep learning methods to find
out how the size and diversity of the datasets affect deep models' performance.
Finally, a systematic survey to the existing efforts to solve the data hunger
problem is conducted on both methodological and dataset's viewpoints, followed
by an insightful discussion of remaining problems and open questions To the
best of our knowledge, this is the first work to analyze the data hunger
problem for 3D semantic segmentation using deep learning techniques that are
addressed in the literature review, statistical analysis, and cross-dataset and
cross-algorithm experiments. We share findings and discussions, which may lead
to potential topics in future works.Comment: 22 pages, 15 figure
An \mathcal{O}(N) Maxwell solver with improved numerical dispersion properties
A Maxwell solver derived from finite element method with \mathcal{O}(N)
computing cost is developed to improve the numerical dispersion properties in
relativistic particle-in-cell (PIC) simulations. The correction of the
dispersion relation of the electromagnetic wave is achieved using the
neighboring cells via an iteration scheme without decomposing into Fourier
modes. The local nature of the communication is ideally suited to massively
parallel computer architectures. This Maxwell solver constrains the Numerical
Cherenkov instability (NCI) for the ultra-relativistic drifting pair plasma in
x direction to large wave vectors for two dimensional grid. The growth rate of
NCI is suppressed by using the low pass filtering.Comment: 16 pages, 4 figures, submitted to Computer Physics Communication
SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic Instances
3D semantic segmentation is one of the key tasks for autonomous driving
system. Recently, deep learning models for 3D semantic segmentation task have
been widely researched, but they usually require large amounts of training
data. However, the present datasets for 3D semantic segmentation are lack of
point-wise annotation, diversiform scenes and dynamic objects.
In this paper, we propose the SemanticPOSS dataset, which contains 2988
various and complicated LiDAR scans with large quantity of dynamic instances.
The data is collected in Peking University and uses the same data format as
SemanticKITTI. In addition, we evaluate several typical 3D semantic
segmentation models on our SemanticPOSS dataset. Experimental results show that
SemanticPOSS can help to improve the prediction accuracy of dynamic objects as
people, car in some degree. SemanticPOSS will be published at
\url{www.poss.pku.edu.cn}.Comment: submited to IEEE Intelligent Vehicles Symposium(2020
Metastable magnetic bubble in [Co/Pd]4/Py multilayers
Magnetic bubbles are topologically spin textures that offering the
interesting physics and great promise for next-generation information storage
technologies. The main obstacles so far are that magnetic bubbles are generated
with no field stimuli in new material systems at room temperature. Here, we
report the observation of individual magnetic bubbles and its high frequency
measurement at room temperature in an exchange-coupled [Co/Pd]4/Py multilayers.
We demonstrate that the emergence of magnetic bubbles at remanence can be tuned
by the in-plane tilted magnetic field (roughly 3{\deg}) along the film plane at
room temperature. High frequency results indicate that the presence of magnetic
bubbles leads to broadening of the magnetic permeability spectrum lines (due to
the non-uniformity of the magnetic moments). Our findings open the door to the
bubble-based spintronics at room temperature in exchange-coupled magnetic
multilayers.Comment: 15 pages, 7 figure
Human Correspondence Consensus for 3D Object Semantic Understanding
Semantic understanding of 3D objects is crucial in many applications such as
object manipulation. However, it is hard to give a universal definition of
point-level semantics that everyone would agree on. We observe that people have
a consensus on semantic correspondences between two areas from different
objects, but are less certain about the exact semantic meaning of each area.
Therefore, we argue that by providing human labeled correspondences between
different objects from the same category instead of explicit semantic labels,
one can recover rich semantic information of an object. In this paper, we
introduce a new dataset named CorresPondenceNet. Based on this dataset, we are
able to learn dense semantic embeddings with a novel geodesic consistency loss.
Accordingly, several state-of-the-art networks are evaluated on this
correspondence benchmark. We further show that CorresPondenceNet could not only
boost fine-grained understanding of heterogeneous objects but also cross-object
registration and partial object matching.Comment: 18 pages; ECCV 202
KeypointNet: A Large-scale 3D Keypoint Dataset Aggregated from Numerous Human Annotations
Detecting 3D objects keypoints is of great interest to the areas of both
graphics and computer vision. There have been several 2D and 3D keypoint
datasets aiming to address this problem in a data-driven way. These datasets,
however, either lack scalability or bring ambiguity to the definition of
keypoints. Therefore, we present KeypointNet: the first large-scale and diverse
3D keypoint dataset that contains 103,450 keypoints and 8,234 3D models from 16
object categories, by leveraging numerous human annotations. To handle the
inconsistency between annotations from different people, we propose a novel
method to aggregate these keypoints automatically, through minimization of a
fidelity loss. Finally, ten state-of-the-art methods are benchmarked on our
proposed dataset. Our code and data are available on
https://github.com/qq456cvb/KeypointNet.Comment: 8 pages; to appear in CVPR 202
Systematic investigations of positive-parity doublet bands with three-quasiparticle configurations in Cs
The experimental features of positive-parity doublet bands in the
odd-\emph{A} cesium isotopes Cs, including angular momentum
alignment, energy staggering, etc. are studied systematically and
compared to those of the candidate chiral bands in the adjacent odd-odd Cs
isotopes. The configuration assignments and the dynamics of these bands are
discussed. The self-consistent tilted axis cranking relativistic mean-field
calculations are performed with configuration reassigned to these bands. The
experimental level schemes of four nuclei are well reproduced, and the
calculations also show four nuclei have obvious triaxial deformations and thus
support the candidate chiral doublet bands in Cs.Comment: 18 pages, 10 figure
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