118 research outputs found
CARNet:Compression Artifact Reduction for Point Cloud Attribute
A learning-based adaptive loop filter is developed for the Geometry-based
Point Cloud Compression (G-PCC) standard to reduce attribute compression
artifacts. The proposed method first generates multiple Most-Probable Sample
Offsets (MPSOs) as potential compression distortion approximations, and then
linearly weights them for artifact mitigation. As such, we drive the filtered
reconstruction as close to the uncompressed PCA as possible. To this end, we
devise a Compression Artifact Reduction Network (CARNet) which consists of two
consecutive processing phases: MPSOs derivation and MPSOs combination. The
MPSOs derivation uses a two-stream network to model local neighborhood
variations from direct spatial embedding and frequency-dependent embedding,
where sparse convolutions are utilized to best aggregate information from
sparsely and irregularly distributed points. The MPSOs combination is guided by
the least square error metric to derive weighting coefficients on the fly to
further capture content dynamics of input PCAs. The CARNet is implemented as an
in-loop filtering tool of the GPCC, where those linear weighting coefficients
are encapsulated into the bitstream with negligible bit rate overhead.
Experimental results demonstrate significant improvement over the latest GPCC
both subjectively and objectively.Comment: 13pages, 8figure
IAIFNet: An Illumination-Aware Infrared and Visible Image Fusion Network
Infrared and visible image fusion (IVIF) is used to generate fusion images
with comprehensive features of both images, which is beneficial for downstream
vision tasks. However, current methods rarely consider the illumination
condition in low-light environments, and the targets in the fused images are
often not prominent. To address the above issues, we propose an
Illumination-Aware Infrared and Visible Image Fusion Network, named as IAIFNet.
In our framework, an illumination enhancement network first estimates the
incident illumination maps of input images. Afterwards, with the help of
proposed adaptive differential fusion module (ADFM) and salient target aware
module (STAM), an image fusion network effectively integrates the salient
features of the illumination-enhanced infrared and visible images into a fusion
image of high visual quality. Extensive experimental results verify that our
method outperforms five state-of-the-art methods of fusing infrared and visible
images.Comment: Submitted to IEE
SSPFusion: A Semantic Structure-Preserving Approach for Infrared and Visible Image Fusion
Most existing learning-based infrared and visible image fusion (IVIF) methods
exhibit massive redundant information in the fusion images, i.e., yielding
edge-blurring effect or unrecognizable for object detectors. To alleviate these
issues, we propose a semantic structure-preserving approach for IVIF, namely
SSPFusion. At first, we design a Structural Feature Extractor (SFE) to extract
the structural features of infrared and visible images. Then, we introduce a
multi-scale Structure-Preserving Fusion (SPF) module to fuse the structural
features of infrared and visible images, while maintaining the consistency of
semantic structures between the fusion and source images. Owing to these two
effective modules, our method is able to generate high-quality fusion images
from pairs of infrared and visible images, which can boost the performance of
downstream computer-vision tasks. Experimental results on three benchmarks
demonstrate that our method outperforms eight state-of-the-art image fusion
methods in terms of both qualitative and quantitative evaluations. The code for
our method, along with additional comparison results, will be made available
at: https://github.com/QiaoYang-CV/SSPFUSION.Comment: Submitted to IEE
ExtrudeNet: Unsupervised Inverse Sketch-and-Extrude for Shape Parsing
Sketch-and-extrude is a common and intuitive modeling process in computer
aided design. This paper studies the problem of learning the shape given in the
form of point clouds by inverse sketch-and-extrude. We present ExtrudeNet, an
unsupervised end-to-end network for discovering sketch and extrude from point
clouds. Behind ExtrudeNet are two new technical components: 1) an effective
representation for sketch and extrude, which can model extrusion with freeform
sketches and conventional cylinder and box primitives as well; and 2) a
numerical method for computing the signed distance field which is used in the
network learning. This is the first attempt that uses machine learning to
reverse engineer the sketch-and-extrude modeling process of a shape in an
unsupervised fashion. ExtrudeNet not only outputs a compact, editable and
interpretable representation of the shape that can be seamlessly integrated
into modern CAD software, but also aligns with the standard CAD modeling
process facilitating various editing applications, which distinguishes our work
from existing shape parsing research. Code is released at
https://github.com/kimren227/ExtrudeNet.Comment: Accepted to ECCV 202
Efficient Memory Management for GPU-based Deep Learning Systems
GPU (graphics processing unit) has been used for many data-intensive
applications. Among them, deep learning systems are one of the most important
consumer systems for GPU nowadays. As deep learning applications impose deeper
and larger models in order to achieve higher accuracy, memory management
becomes an important research topic for deep learning systems, given that GPU
has limited memory size. Many approaches have been proposed towards this issue,
e.g., model compression and memory swapping. However, they either degrade the
model accuracy or require a lot of manual intervention. In this paper, we
propose two orthogonal approaches to reduce the memory cost from the system
perspective. Our approaches are transparent to the models, and thus do not
affect the model accuracy. They are achieved by exploiting the iterative nature
of the training algorithm of deep learning to derive the lifetime and
read/write order of all variables. With the lifetime semantics, we are able to
implement a memory pool with minimal fragments. However, the optimization
problem is NP-complete. We propose a heuristic algorithm that reduces up to
13.3% of memory compared with Nvidia's default memory pool with equal time
complexity. With the read/write semantics, the variables that are not in use
can be swapped out from GPU to CPU to reduce the memory footprint. We propose
multiple swapping strategies to automatically decide which variable to swap and
when to swap out (in), which reduces the memory cost by up to 34.2% without
communication overhead
Efficient Memory Management for GPU-based Deep Learning Systems
GPU (graphics processing unit) has been used for many data-intensive
applications. Among them, deep learning systems are one of the most important
consumer systems for GPU nowadays. As deep learning applications impose deeper
and larger models in order to achieve higher accuracy, memory management
becomes an important research topic for deep learning systems, given that GPU
has limited memory size. Many approaches have been proposed towards this issue,
e.g., model compression and memory swapping. However, they either degrade the
model accuracy or require a lot of manual intervention. In this paper, we
propose two orthogonal approaches to reduce the memory cost from the system
perspective. Our approaches are transparent to the models, and thus do not
affect the model accuracy. They are achieved by exploiting the iterative nature
of the training algorithm of deep learning to derive the lifetime and
read/write order of all variables. With the lifetime semantics, we are able to
implement a memory pool with minimal fragments. However, the optimization
problem is NP-complete. We propose a heuristic algorithm that reduces up to
13.3% of memory compared with Nvidia's default memory pool with equal time
complexity. With the read/write semantics, the variables that are not in use
can be swapped out from GPU to CPU to reduce the memory footprint. We propose
multiple swapping strategies to automatically decide which variable to swap and
when to swap out (in), which reduces the memory cost by up to 34.2% without
communication overhead
Variational Relational Point Completion Network for Robust 3D Classification
Real-scanned point clouds are often incomplete due to viewpoint, occlusion,
and noise, which hampers 3D geometric modeling and perception. Existing point
cloud completion methods tend to generate global shape skeletons and hence lack
fine local details. Furthermore, they mostly learn a deterministic
partial-to-complete mapping, but overlook structural relations in man-made
objects. To tackle these challenges, this paper proposes a variational
framework, Variational Relational point Completion Network (VRCNet) with two
appealing properties: 1) Probabilistic Modeling. In particular, we propose a
dual-path architecture to enable principled probabilistic modeling across
partial and complete clouds. One path consumes complete point clouds for
reconstruction by learning a point VAE. The other path generates complete
shapes for partial point clouds, whose embedded distribution is guided by
distribution obtained from the reconstruction path during training. 2)
Relational Enhancement. Specifically, we carefully design point self-attention
kernel and point selective kernel module to exploit relational point features,
which refines local shape details conditioned on the coarse completion. In
addition, we contribute multi-view partial point cloud datasets (MVP and MVP-40
dataset) containing over 200,000 high-quality scans, which render partial 3D
shapes from 26 uniformly distributed camera poses for each 3D CAD model.
Extensive experiments demonstrate that VRCNet outperforms state-of-the-art
methods on all standard point cloud completion benchmarks. Notably, VRCNet
shows great generalizability and robustness on real-world point cloud scans.
Moreover, we can achieve robust 3D classification for partial point clouds with
the help of VRCNet, which can highly increase classification accuracy.Comment: 12 pages, 10 figures, accepted by PAMI. project webpage:
https://mvp-dataset.github.io/. arXiv admin note: substantial text overlap
with arXiv:2104.1015
Comparative pulmonary toxicity of two Ceria nanoparticles with the same primary size
Ceria nanoparticles (nano-ceria) have recently gained a wide range of applications, which might pose unwanted risks to both the environment and human health. The greatest potential for the environmental discharge of nano-ceria appears to be in their use as a diesel fuel additive. The present study was designed to explore the pulmonary toxicity of nano-ceria in mice after a single exposure via intratracheal instillation. Two types of nano-ceria with the same distribution of a primary size (3–5 nm), but different redox activity, were used: Ceria-p, synthesized by a precipitation route, and Ceria-h, synthesized by a hydrothermal route. Both Ceria-p and Ceria-h induced oxidative stress, inflammatory responses and cytotoxicity in mice, but their toxicological profiles were quite different. The mean size of Ceria-p agglomerates was much smaller compared to Ceria-h, thereby causing a more potent acute inflammation, due to their higher number concentration of agglomerates and higher deposition rate in the deep lung. Ceria-h had a higher reactivity to catalyzing the generation of reactive oxygen species (ROS), and caused two waves of lung injury: bronchoalveolar lavage (BAL) inflammation and cytotoxicity in the early stage and redox-activity-evoked lipid peroxidation and pro-inflammation in the latter stage. Therefore, the size distribution of ceria-containing agglomerates in the exhaust, as well as their surface chemistry are essential characteristics to assess the potential risks of using nano-ceria as a fuel additive
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