294 research outputs found
Planar Prior Assisted PatchMatch Multi-View Stereo
The completeness of 3D models is still a challenging problem in multi-view
stereo (MVS) due to the unreliable photometric consistency in low-textured
areas. Since low-textured areas usually exhibit strong planarity, planar models
are advantageous to the depth estimation of low-textured areas. On the other
hand, PatchMatch multi-view stereo is very efficient for its sampling and
propagation scheme. By taking advantage of planar models and PatchMatch
multi-view stereo, we propose a planar prior assisted PatchMatch multi-view
stereo framework in this paper. In detail, we utilize a probabilistic graphical
model to embed planar models into PatchMatch multi-view stereo and contribute a
novel multi-view aggregated matching cost. This novel cost takes both
photometric consistency and planar compatibility into consideration, making it
suited for the depth estimation of both non-planar and planar regions.
Experimental results demonstrate that our method can efficiently recover the
depth information of extremely low-textured areas, thus obtaining high complete
3D models and achieving state-of-the-art performance.Comment: Accepted by AAAI-202
Learning Inverse Depth Regression for Multi-View Stereo with Correlation Cost Volume
Deep learning has shown to be effective for depth inference in multi-view
stereo (MVS). However, the scalability and accuracy still remain an open
problem in this domain. This can be attributed to the memory-consuming cost
volume representation and inappropriate depth inference. Inspired by the
group-wise correlation in stereo matching, we propose an average group-wise
correlation similarity measure to construct a lightweight cost volume. This can
not only reduce the memory consumption but also reduce the computational burden
in the cost volume filtering. Based on our effective cost volume
representation, we propose a cascade 3D U-Net module to regularize the cost
volume to further boost the performance. Unlike the previous methods that treat
multi-view depth inference as a depth regression problem or an inverse depth
classification problem, we recast multi-view depth inference as an inverse
depth regression task. This allows our network to achieve sub-pixel estimation
and be applicable to large-scale scenes. Through extensive experiments on DTU
dataset and Tanks and Temples dataset, we show that our proposed network with
Correlation cost volume and Inverse DEpth Regression (CIDER), achieves
state-of-the-art results, demonstrating its superior performance on scalability
and accuracy.Comment: Accepted by AAAI-202
The relationship among customer demand, competitive strategy and manufacturing system functional objectives
Purpose: To ascertain the relationship between the operation system function goal decision making and customer demand and competition strategy, can better discover and integrate all available resources (including important capital resources) to achieve business opportunities, the establishment of sustainable competitive ability. Because, to achieve business development lead policymakers take great uncertainty, which led to the investment behavior required for the operational activities of resources also bear the enormous risks.
Design/methodology/approach: Through principal component analysis on the data collected by questionnaires, the manuscript obtains dominant factors for customer demand, competitive strategy and manufacturing system functional objectives respectively. By these factors, it tests its three hypotheses with the data from northeast of China and draws some conclusions.
Findings: The results show that customer demand have a significant positive effect on competitive strategy; competitive strategy have positive influence on manufacturing system functional objectives; customer demand affect the functional objectives, by competitive strategy.
Research limitations/implications: In this research, competitive strategy and manufacturing system functional objectives are influenced by customer demand. The conclusion of the research can provide theoretical guidance for Chinese enterprises which carry out manufacturing system functional objectives.
Originality/value: In this research, a new measure questionnaire of competition strategy, customer satisfaction and operating system function goal was used, analyzed the influence factors of time, quality, cost, efficiency, service and environment, on the operation of the system. The study shows that the effect of competition strategy and customer demand has a direct impact on the operating system functions, customer demand through competitive strategy of indirect effects operating system functions.Peer Reviewe
PG-NeuS: Robust and Efficient Point Guidance for Multi-View Neural Surface Reconstruction
Recently, learning multi-view neural surface reconstruction with the
supervision of point clouds or depth maps has been a promising way. However,
due to the underutilization of prior information, current methods still
struggle with the challenges of limited accuracy and excessive time complexity.
In addition, prior data perturbation is also an important but rarely considered
issue. To address these challenges, we propose a novel point-guided method
named PG-NeuS, which achieves accurate and efficient reconstruction while
robustly coping with point noise. Specifically, aleatoric uncertainty of the
point cloud is modeled to capture the distribution of noise, leading to noise
robustness. Furthermore, a Neural Projection module connecting points and
images is proposed to add geometric constraints to implicit surface, achieving
precise point guidance. To better compensate for geometric bias between volume
rendering and point modeling, high-fidelity points are filtered into a Bias
Network to further improve details representation. Benefiting from the
effective point guidance, even with a lightweight network, the proposed PG-NeuS
achieves fast convergence with an impressive 11x speedup compared to NeuS.
Extensive experiments show that our method yields high-quality surfaces with
high efficiency, especially for fine-grained details and smooth regions,
outperforming the state-of-the-art methods. Moreover, it exhibits strong
robustness to noisy data and sparse data
Reducing Spurious Correlations for Aspect-Based Sentiment Analysis with Variational Information Bottleneck and Contrastive Learning
Deep learning techniques have dominated the literature on aspect-based
sentiment analysis (ABSA), yielding state-of-the-art results. However, these
deep models generally suffer from spurious correlation problems between input
features and output labels, which creates significant barriers to robustness
and generalization capability. In this paper, we propose a novel Contrastive
Variational Information Bottleneck framework (called CVIB) to reduce spurious
correlations for ABSA. The proposed CVIB framework is composed of an original
network and a self-pruned network, and these two networks are optimized
simultaneously via contrastive learning. Concretely, we employ the Variational
Information Bottleneck (VIB) principle to learn an informative and compressed
network (self-pruned network) from the original network, which discards the
superfluous patterns or spurious correlations between input features and
prediction labels. Then, self-pruning contrastive learning is devised to pull
together semantically similar positive pairs and push away dissimilar pairs,
where the representations of the anchor learned by the original and self-pruned
networks respectively are regarded as a positive pair while the representations
of two different sentences within a mini-batch are treated as a negative pair.
To verify the effectiveness of our CVIB method, we conduct extensive
experiments on five benchmark ABSA datasets and the experimental results show
that our approach achieves better performance than the strong competitors in
terms of overall prediction performance, robustness, and generalization
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