42 research outputs found
LRF-Net: Learning Local Reference Frames for 3D Local Shape Description and Matching
The local reference frame (LRF) acts as a critical role in 3D local shape
description and matching. However, most of existing LRFs are hand-crafted and
suffer from limited repeatability and robustness. This paper presents the first
attempt to learn an LRF via a Siamese network that needs weak supervision only.
In particular, we argue that each neighboring point in the local surface gives
a unique contribution to LRF construction and measure such contributions via
learned weights. Extensive analysis and comparative experiments on three public
datasets addressing different application scenarios have demonstrated that
LRF-Net is more repeatable and robust than several state-of-the-art LRF methods
(LRF-Net is only trained on one dataset). In addition, LRF-Net can
significantly boost the local shape description and 6-DoF pose estimation
performance when matching 3D point clouds.Comment: 28 pages, 14 figure
Learning Second-Order Attentive Context for Efficient Correspondence Pruning
Correspondence pruning aims to search consistent correspondences (inliers)
from a set of putative correspondences. It is challenging because of the
disorganized spatial distribution of numerous outliers, especially when
putative correspondences are largely dominated by outliers. It's more
challenging to ensure effectiveness while maintaining efficiency. In this
paper, we propose an effective and efficient method for correspondence pruning.
Inspired by the success of attentive context in correspondence problems, we
first extend the attentive context to the first-order attentive context and
then introduce the idea of attention in attention (ANA) to model second-order
attentive context for correspondence pruning. Compared with first-order
attention that focuses on feature-consistent context, second-order attention
dedicates to attention weights itself and provides an additional source to
encode consistent context from the attention map. For efficiency, we derive two
approximate formulations for the naive implementation of second-order attention
to optimize the cubic complexity to linear complexity, such that second-order
attention can be used with negligible computational overheads. We further
implement our formulations in a second-order context layer and then incorporate
the layer in an ANA block. Extensive experiments demonstrate that our method is
effective and efficient in pruning outliers, especially in high-outlier-ratio
cases. Compared with the state-of-the-art correspondence pruning approach
LMCNet, our method runs 14 times faster while maintaining a competitive
accuracy.Comment: 9 pages, 8 figures; Accepted to AAAI 2023 (Oral
Learning Probabilistic Coordinate Fields for Robust Correspondences
We introduce Probabilistic Coordinate Fields (PCFs), a novel
geometric-invariant coordinate representation for image correspondence
problems. In contrast to standard Cartesian coordinates, PCFs encode
coordinates in correspondence-specific barycentric coordinate systems (BCS)
with affine invariance. To know \textit{when and where to trust} the encoded
coordinates, we implement PCFs in a probabilistic network termed PCF-Net, which
parameterizes the distribution of coordinate fields as Gaussian mixture models.
By jointly optimizing coordinate fields and their confidence conditioned on
dense flows, PCF-Net can work with various feature descriptors when quantifying
the reliability of PCFs by confidence maps. An interesting observation of this
work is that the learned confidence map converges to geometrically coherent and
semantically consistent regions, which facilitates robust coordinate
representation. By delivering the confident coordinates to keypoint/feature
descriptors, we show that PCF-Net can be used as a plug-in to existing
correspondence-dependent approaches. Extensive experiments on both indoor and
outdoor datasets suggest that accurate geometric invariant coordinates help to
achieve the state of the art in several correspondence problems, such as sparse
feature matching, dense image registration, camera pose estimation, and
consistency filtering. Further, the interpretable confidence map predicted by
PCF-Net can also be leveraged to other novel applications from texture transfer
to multi-homography classification.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine
Intelligenc
Point-and-Shoot All-in-Focus Photo Synthesis from Smartphone Camera Pair
All-in-Focus (AIF) photography is expected to be a commercial selling point
for modern smartphones. Standard AIF synthesis requires manual, time-consuming
operations such as focal stack compositing, which is unfriendly to ordinary
people. To achieve point-and-shoot AIF photography with a smartphone, we expect
that an AIF photo can be generated from one shot of the scene, instead of from
multiple photos captured by the same camera. Benefiting from the multi-camera
module in modern smartphones, we introduce a new task of AIF synthesis from
main (wide) and ultra-wide cameras. The goal is to recover sharp details from
defocused regions in the main-camera photo with the help of the
ultra-wide-camera one. The camera setting poses new challenges such as
parallax-induced occlusions and inconsistent color between cameras. To overcome
the challenges, we introduce a predict-and-refine network to mitigate
occlusions and propose dynamic frequency-domain alignment for color correction.
To enable effective training and evaluation, we also build an AIF dataset with
2686 unique scenes. Each scene includes two photos captured by the main camera,
one photo captured by the ultrawide camera, and a synthesized AIF photo.
Results show that our solution, termed EasyAIF, can produce high-quality AIF
photos and outperforms strong baselines quantitatively and qualitatively. For
the first time, we demonstrate point-and-shoot AIF photo synthesis successfully
from main and ultra-wide cameras.Comment: Early Access by IEEE Transactions on Circuits and Systems for Video
Technology 202
Constraining Depth Map Geometry for Multi-View Stereo: A Dual-Depth Approach with Saddle-shaped Depth Cells
Learning-based multi-view stereo (MVS) methods deal with predicting accurate
depth maps to achieve an accurate and complete 3D representation. Despite the
excellent performance, existing methods ignore the fact that a suitable depth
geometry is also critical in MVS. In this paper, we demonstrate that different
depth geometries have significant performance gaps, even using the same depth
prediction error. Therefore, we introduce an ideal depth geometry composed of
Saddle-Shaped Cells, whose predicted depth map oscillates upward and downward
around the ground-truth surface, rather than maintaining a continuous and
smooth depth plane. To achieve it, we develop a coarse-to-fine framework called
Dual-MVSNet (DMVSNet), which can produce an oscillating depth plane.
Technically, we predict two depth values for each pixel (Dual-Depth), and
propose a novel loss function and a checkerboard-shaped selecting strategy to
constrain the predicted depth geometry. Compared to existing methods,DMVSNet
achieves a high rank on the DTU benchmark and obtains the top performance on
challenging scenes of Tanks and Temples, demonstrating its strong performance
and generalization ability. Our method also points to a new research direction
for considering depth geometry in MVS.Comment: Accepted by ICCV 202
Fast Full-frame Video Stabilization with Iterative Optimization
Video stabilization refers to the problem of transforming a shaky video into
a visually pleasing one. The question of how to strike a good trade-off between
visual quality and computational speed has remained one of the open challenges
in video stabilization. Inspired by the analogy between wobbly frames and
jigsaw puzzles, we propose an iterative optimization-based learning approach
using synthetic datasets for video stabilization, which consists of two
interacting submodules: motion trajectory smoothing and full-frame outpainting.
First, we develop a two-level (coarse-to-fine) stabilizing algorithm based on
the probabilistic flow field. The confidence map associated with the estimated
optical flow is exploited to guide the search for shared regions through
backpropagation. Second, we take a divide-and-conquer approach and propose a
novel multiframe fusion strategy to render full-frame stabilized views. An
important new insight brought about by our iterative optimization approach is
that the target video can be interpreted as the fixed point of nonlinear
mapping for video stabilization. We formulate video stabilization as a problem
of minimizing the amount of jerkiness in motion trajectories, which guarantees
convergence with the help of fixed-point theory. Extensive experimental results
are reported to demonstrate the superiority of the proposed approach in terms
of computational speed and visual quality. The code will be available on
GitHub.Comment: Accepted by ICCV202
PL-015 Aerobic exercise increases BKCa channel expression to enhance tracheal smooth muscle relaxation in a murine asthma model: There is no full paper associated with this abstract
Objective Increasing evidence has shown that moderate-intensity aerobic exercise training reduces airway hyperresponsiveness (AHR) in patients with asthma. However, the mechanisms underlying exercise-induced improvements in smooth muscle contractility have not been fully elucidated. Large-conductance Ca2+-activated K+ channels (BKCa) are expressed broadly on smooth muscle cells and play an important role in the regulation of smooth muscle contraction. We tested the hypothesis that exercise training increases the contribution of BKCa channel to tracheal smooth muscle relaxation in in ovalbumin (OVA)-challenged asthmatic rats.
Methods Rats were sensitized/challenged with OVA or saline and exercised at a moderate intensity 5 times/week for 4 weeks. Tracheal smooth muscle contractility was tested. Membrane potential of primary cultured tracheal smooth muscle cells was measured. In addition, western immunoblotting was performed to study the expression levels of BKCa channel protein.
Results The contraction of rat airway smooth muscle induced by carbachol was significantly increased with asthma and exercise training reversed this alteration. Application of BKCa channel agonist, NS1619, induced tracheal smooth muscle relaxation. NS1619-induced relaxation was decreased in asthmatic rats, however exercise training significantly increased NS1619-induced relaxation. In primary cultured smooth muscle cells, NS1619-induced membrane potential was reduced with asthma and this alteration was diminished after exercise training. Additionally, western blotting revealed that the protein expression of BKCa was reduced in asthmatic group and aerobic exercise significantly improved BKCa expression.
Conclusions The present study reveals that aerobic exercise training increases BKCa expression on tracheal smooth muscle, which partly underlies the beneficial effect of exercise on improving airway smooth muscle relaxation in asthma
Annual precipitation and daily extreme precipitation distribution: possible trends from 1960 to 2010 in urban areas of China
With global warming, precipitation events are often prone to intensify in some regions. Understanding the changing characteristics of annual and daily extreme precipitation as well as the underlying mechanisms plays an import role for early warning of precipitation-induced disaster (e.g. floods, landslides) and water resources management, especially in densely populated urban areas. In this study, we investigate the long-term trend of annual and daily extreme precipitation in China during 1960–2010 based on daily observations from 539 meteorological stations, and the land cover map with impervious information. We find an overall increasing trend in annual and daily extreme precipitation, particularly in South-East and North-West of China. Moreover, 157 stations located in metropolitan regions experience higher increasing trends of daily extreme precipitation, particularly in Shanghai and Guangzhou metropolitan areas. It is noted that the central urban area of one metropolitan region may have significantly higher increasing trends of daily extreme precipitation than corresponding surrounding areas
Anthropogenic Activities Generate High-Refractory Black Carbon along the Yangtze River Continuum
12 pages, 7 figuresCombustion-driven particulate black carbon (PBC) is a crucial slow-cycling pool in the organic carbon flux from rivers to oceans. Since the refractoriness of PBC stems from the association of non-homologous char and soot, the composition and source of char and soot must be considered when investigating riverine PBC. Samples along the Yangtze River continuum during different hydrological periods were collected in this study to investigate the association and asynchronous combustion drive of char and soot in PBC. The results revealed that PBC in the Yangtze River, with higher refractory nature, accounts for 13.73 ± 6.89% of particulate organic carbon, and soot occupies 37.53 ± 11.00% of PBC. The preponderant contribution of fossil fuel combustion to soot (92.57 ± 3.20%) compared to char (27.55 ± 5.92%), suggested that fossil fuel combustion is a crucial driver for PBC with high soot percentage. Redundancy analysis and structural equation modeling confirmed that the fossil fuel energy used by anthropogenic activities promoting soot is the crucial reason for high-refractory PBC. We estimated that the Yangtze River transported 0.15–0.23 Tg of soot and 0.15–0.25 Tg of char to the ocean annually, and the export of large higher refractory PBC to the ocean can form a long-term sink and prolong the residence time of terrigenous carbonThis study was supported by grants from the National Natural Science Foundation of China (nos. 42277214, 42207256, and 41971286), major programs of the National Social Science Foundation of China (grant nos. 22&ZD136), the Special Science and Technology Innovation Program for Carbon Peak and Carbon Neutralization of Jiangsu Province (grant no. BE2022612)Peer reviewe