465 research outputs found
SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimation
Depth estimation from images serves as the fundamental step of 3D perception
for autonomous driving and is an economical alternative to expensive depth
sensors like LiDAR. The temporal photometric constraints enables
self-supervised depth estimation without labels, further facilitating its
application. However, most existing methods predict the depth solely based on
each monocular image and ignore the correlations among multiple surrounding
cameras, which are typically available for modern self-driving vehicles. In
this paper, we propose a SurroundDepth method to incorporate the information
from multiple surrounding views to predict depth maps across cameras.
Specifically, we employ a joint network to process all the surrounding views
and propose a cross-view transformer to effectively fuse the information from
multiple views. We apply cross-view self-attention to efficiently enable the
global interactions between multi-camera feature maps. Different from
self-supervised monocular depth estimation, we are able to predict real-world
scales given multi-camera extrinsic matrices. To achieve this goal, we adopt
the two-frame structure-from-motion to extract scale-aware pseudo depths to
pretrain the models. Further, instead of predicting the ego-motion of each
individual camera, we estimate a universal ego-motion of the vehicle and
transfer it to each view to achieve multi-view ego-motion consistency. In
experiments, our method achieves the state-of-the-art performance on the
challenging multi-camera depth estimation datasets DDAD and nuScenes.Comment: Accepted to CoRL 2022. Project page:
https://surrounddepth.ivg-research.xyz Code:
https://github.com/weiyithu/SurroundDept
Weighted Monte Carlo augmented spherical Fourier-Bessel convolutional layers for 3D abdominal organ segmentation
Filter-decomposition-based group equivariant convolutional neural networks
show promising stability and data efficiency for 3D image feature extraction.
However, the existing filter-decomposition-based 3D group equivariant neural
networks rely on parameter-sharing designs and are mostly limited to rotation
transformation groups, where the chosen spherical harmonic filter bases
consider only angular orthogonality. These limitations hamper its application
to deep neural network architectures for medical image segmentation. To address
these issues, this paper describes a non-parameter-sharing affine group
equivariant neural network for 3D medical image segmentation based on an
adaptive aggregation of Monte Carlo augmented spherical Fourier Bessel filter
bases. The efficiency and flexibility of the adopted non-parameter-sharing
strategy enable for the first time an efficient implementation of 3D affine
group equivariant convolutional neural networks for volumetric data. The
introduced spherical Bessel Fourier filter basis combines both angular and
radial orthogonality for better feature extraction. The 3D image segmentation
experiments on two abdominal medical image sets, BTCV and the NIH Pancreas
datasets, show that the proposed methods excel the state-of-the-art 3D neural
networks with high training stability and data efficiency. The code will be
available at https://github.com/ZhaoWenzhao/WMCSFB
The Chinese pine genome and methylome unveil key features of conifer evolution
Conifers dominate the world's forest ecosystems and are the most widely planted tree species. Their giant and complex genomes present great challenges for assembling a complete reference genome for evolutionary and genomic studies. We present a 25.4-Gb chromosome-level assembly of Chinese pine (Pinus tabuliformis) and revealed that its genome size is mostly attributable to huge intergenic regions and long introns with high transposable element (TE) content. Large genes with long introns exhibited higher expressions levels. Despite a lack of recent whole-genome duplication, 91.2% of genes were duplicated through dispersed duplication, and expanded gene families are mainly related to stress responses, which may underpin conifers' adaptation, particularly in cold and/or arid conditions. The reproductive regulation network is distinct compared with angiosperms. Slow removal of TEs with high-level methylation may have contributed to genomic expansion. This study provides insights into conifer evolution and resources for advancing research on conifer adaptation and development
Insight-HXMT observations of Swift J0243.6+6124 during its 2017-2018 outburst
The recently discovered neutron star transient Swift J0243.6+6124 has been
monitored by {\it the Hard X-ray Modulation Telescope} ({\it Insight-\rm HXMT).
Based on the obtained data, we investigate the broadband spectrum of the source
throughout the outburst. We estimate the broadband flux of the source and
search for possible cyclotron line in the broadband spectrum. No evidence of
line-like features is, however, found up to . In the absence of
any cyclotron line in its energy spectrum, we estimate the magnetic field of
the source based on the observed spin evolution of the neutron star by applying
two accretion torque models. In both cases, we get consistent results with
, and peak luminosity of which makes the source the first Galactic ultraluminous
X-ray source hosting a neutron star.Comment: publishe
Overview to the Hard X-ray Modulation Telescope (Insight-HXMT) Satellite
As China's first X-ray astronomical satellite, the Hard X-ray Modulation
Telescope (HXMT), which was dubbed as Insight-HXMT after the launch on June 15,
2017, is a wide-band (1-250 keV) slat-collimator-based X-ray astronomy
satellite with the capability of all-sky monitoring in 0.2-3 MeV. It was
designed to perform pointing, scanning and gamma-ray burst (GRB) observations
and, based on the Direct Demodulation Method (DDM), the image of the scanned
sky region can be reconstructed. Here we give an overview of the mission and
its progresses, including payload, core sciences, ground calibration/facility,
ground segment, data archive, software, in-orbit performance, calibration,
background model, observations and some preliminary results.Comment: 29 pages, 40 figures, 6 tables, to appear in Sci. China-Phys. Mech.
Astron. arXiv admin note: text overlap with arXiv:1910.0443
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