RayMVSNet++: Learning Ray-Based 1D Implicit Fields for Accurate Multi-View Stereo

Shi, Yifei; Xi, Junhua; Hu, Dewen; Cai, Zixing; Xu, Kai

doi:10.1109/tpami.2023.3296163

Cited by 36 publications

(8 citation statements)

References 69 publications

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“…Notably, the analysis in Table 1 reveals the presence of varying bond strengths within icosahedral structures. The shortest bonds, represented by the inter-icosahedral i2-i2 bonds with a length of 0.165 Å, suggest their superior strength compared to the intra-icosahedral i-i bonds [ 23 , 24 ]. Conversely, weaker inter-icosahedral bonds, exemplified by i1-i1 (0.192 Å) and i1-i3 (0.204 Å) interactions, are observed.…”

Section: Methods and Findingsmentioning

confidence: 99%

Innovative stain-free technique for high-resolution imaging of virus particles via standard transmission electron microscopy

Thangavelu,

Luis da Silva

2024

Heliyon

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Section: Methods and Findingsmentioning

confidence: 99%

Innovative stain-free technique for high-resolution imaging of virus particles via standard transmission electron microscopy

Thangavelu,

Luis da Silva

2024

Heliyon

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“…When we talk about applications of computer vision, then there are many research works done so far like on wheat classification [ 46 ], brightness correction [ 47 ], pattern analysis [ 48 , 49 ], and photo-synthesis [ 50 ]. The transformer learning models also perform well for target object detection [ 51 – 53 ]. The advancement of Convolutional Neural Networks (CNNs) has spurred the development of various convolution techniques.…”

Section: Related Workmentioning

confidence: 99%

Attention 3D central difference convolutional dense network for hyperspectral image classification

Ashraf,

Alharthi,

Chen

et al. 2024

PLoS ONE

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Hyperspectral Images (HSI) classification is a challenging task due to a large number of spatial-spectral bands of images with high inter-similarity, extra variability classes, and complex region relationships, including overlapping and nested regions. Classification becomes a complex problem in remote sensing images like HSIs. Convolutional Neural Networks (CNNs) have gained popularity in addressing this challenge by focusing on HSI data classification. However, the performance of 2D-CNN methods heavily relies on spatial information, while 3D-CNN methods offer an alternative approach by considering both spectral and spatial information. Nonetheless, the computational complexity of 3D-CNN methods increases significantly due to the large capacity size and spectral dimensions. These methods also face difficulties in manipulating information from local intrinsic detailed patterns of feature maps and low-rank frequency feature tuning. To overcome these challenges and improve HSI classification performance, we propose an innovative approach called the Attention 3D Central Difference Convolutional Dense Network (3D-CDC Attention DenseNet). Our 3D-CDC method leverages the manipulation of local intrinsic detailed patterns in the spatial-spectral features maps, utilizing pixel-wise concatenation and spatial attention mechanism within a dense strategy to incorporate low-rank frequency features and guide the feature tuning. Experimental results on benchmark datasets such as Pavia University, Houston 2018, and Indian Pines demonstrate the superiority of our method compared to other HSI classification methods, including state-of-the-art techniques. The proposed method achieved 97.93% overall accuracy on the Houston-2018, 99.89% on Pavia University, and 99.38% on the Indian Pines dataset with the 25 × 25 window size.

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“…Due to their large effects on a wide range of network performance parameters, including convergence time [59], control overhead ratio [60], power consumption [61,62], and packet delivery rate [63], trickle-down timers have been a popular research issue in the field of network routing. The effort of striking a balance between these measurements is difficult and crucial.…”

Section: Problem Analysismentioning

confidence: 99%

Predictive analysis of COVID 19 disease based on mathematical modelling and machine learning techniques

Rajarajeswari

Santhi²,

Saraswathi³

et al. 2022

Computer Methods in Biomechanics and Biomedical Engineering: Im

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Wireless sensor networks have become incredibly popular due to the Internet of Things' (IoT) rapid development. IoT routing is the basis for the efficient operation of the perception-layer network. As a popular type of machine learning, reinforcement learning techniques have gained significant attention due to their successful application in the field of network communication. In the traditional Routing Protocol for lowpower and Lossy Networks (RPL) protocol, to solve the fairness of control message transmission between IoT terminals, a fair broadcast suppression mechanism, or Drizzle algorithm, is usually used, but the Drizzle algorithm cannot allocate priority. Moreover, the Drizzle algorithm keeps changing its redundant constant k value but never converges to the optimal value of k. To address this problem, this paper uses a combination based on reinforcement learning (RL) and trickle timer. This paper proposes an RL Intelligent Adaptive Trickle-Timer Algorithm (RLATT) for routing optimization of the IoT awareness layer. RLATT has triple-optimized the trickle timer algorithm. To verify the algorithm's effectiveness, the simulation is carried out on Contiki operating system and compared with the standard trickling timer and Drizzle algorithm. Experiments show that the proposed algorithm performs better in terms of packet delivery ratio (PDR), power consumption, network convergence time, and total control cost ratio.

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RayMVSNet++: Learning Ray-Based 1D Implicit Fields for Accurate Multi-View Stereo

Cited by 36 publications

References 69 publications

Innovative stain-free technique for high-resolution imaging of virus particles via standard transmission electron microscopy

Innovative stain-free technique for high-resolution imaging of virus particles via standard transmission electron microscopy

Attention 3D central difference convolutional dense network for hyperspectral image classification

Predictive analysis of COVID 19 disease based on mathematical modelling and machine learning techniques

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