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Point cloud completion via structured feature maps using a feedback network

Computational Visual Media 2023年第1期9卷 71-85页

作者： Zejia Su Haibin Huang Chongyang Ma Hui Huang Ruizhen Hu College of Computer Science&Software Engineering Shenzhen UniversityShenzhenChina Kuaishou Technology China

In this paper,we tackle the challenging problem of point cloud completion from the perspective of feature *** key observation is that to recover the underlying structures as well as surface details,given partial input,a fundamental component is a good feature representation that can capture both global structure and local geometric *** accordingly first propose FSNet,a feature structuring module that can adaptively aggregate point-wise features into a 2D structured feature map by learning multiple latent patterns from local *** then integrate FSNet into a coarse-to-fine pipeline for point cloud ***,a 2D convolutional neural network is adopted to decode feature maps from FSNet into a coarse and complete point ***,a point cloud upsampling network is used to generate a dense point cloud from the partial input and the coarse intermediate *** efficiently exploit local structures and enhance point distribution uniformity,we propose IFNet,a point upsampling module with a self-correction mechanism that can progressively refine details of the generated dense point *** have conducted qualitative and quantitative experiments on ShapeNet,MVP,and KITTI datasets,which demonstrate that our method outperforms stateof-the-art point cloud completion approaches.

关键词： 3D point clouds shape completion geometry processing deep learning

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Active self-training for weakly supervised 3D scene semantic segmentation

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Computational Visual Media 2024年第3期10卷 425-438页

作者： Gengxin Liu Oliver van Kaick Hui Huang Ruizhen Hu College of Computer Science&Software Engineering Shenzhen UniversityShenzhen 518060China School of Computer Science Carleton UniversityOttawa K1S 5B6Canada

Since the preparation of labeled datafor training semantic segmentation networks of pointclouds is a time-consuming process, weakly supervisedapproaches have been introduced to learn fromonly a small fraction of data. These methods aretypically based on learning with contrastive losses whileautomatically deriving per-point pseudo-labels from asparse set of user-annotated labels. In this paper, ourkey observation is that the selection of which samplesto annotate is as important as how these samplesare used for training. Thus, we introduce a methodfor weakly supervised segmentation of 3D scenes thatcombines self-training with active learning. Activelearning selects points for annotation that are likelyto result in improvements to the trained model, whileself-training makes efficient use of the user-providedlabels for learning the model. We demonstrate thatour approach leads to an effective method that providesimprovements in scene segmentation over previouswork and baselines, while requiring only a few userannotations.

关键词： semantic segmentation weakly supervised self-training active learning

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Practical cloud storage auditing using serverless computing

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science China(Information sciences) 2024年第3期67卷 33-47页

作者： Fei CHEN Jianquan CAI Tao XIANG Xiaofeng LIAO College of Computer Science and Software Engineering Shenzhen University College of Computer Science Chongqing University

Cloud storage auditing research is dedicated to solving the data integrity problem of outsourced storage on the cloud. In recent years, researchers have proposed various cloud storage auditing schemes using different techniques. While these studies are elegant in theory, they assume an ideal cloud storage model;that is, they assume that the cloud provides the storage and compute interfaces as required by the proposed schemes. However, this does not hold for mainstream cloud storage systems because these systems only provide read and write interfaces but not the compute interface. To bridge this gap, this work proposes a serverless computing-based cloud storage auditing system for existing mainstream cloud object storage. The proposed system leverages existing cloud storage auditing schemes as a basic building block and makes two adaptations. One is that we use the read interface of cloud object storage to support block data requests in a traditional cloud storage auditing scheme. Another is that we employ the serverless computing paradigm to support block data computation as traditionally required. Leveraging the characteristics of serverless computing, the proposed system realizes economical, pay-as-you-go cloud storage auditing. The proposed system also supports mainstream cloud storage upper layer applications(e.g., file preview) by not modifying the data formats when embedding authentication tags for later auditing. We prototyped and open-sourced the proposed system to a mainstream cloud service, i.e., Tencent Cloud. Experimental results show that the proposed system is efficient and promising for practical use. For 40 GB of data, auditing takes approximately 98 s using serverless computation. The economic cost is 120.48 CNY per year, of which serverless computing only accounts for 46%. In contrast, no existing studies reported cloud storage auditing results for real-world cloud services.

关键词： cloud storage auditing serverless computing object storage usability

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Multi-scale persistent spatiotemporal transformer for long-term urban traffic flow prediction

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Journal of Electronic science and Technology 2024年第1期22卷 53-69页

作者： Jia-Jun Zhong Yong Ma Xin-Zheng Niu Philippe Fournier-Viger Bing Wang Zu-kuan Wei School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China College of Computer Science&Software Engineering Shenzhen UniversityShenzhen518060China School of Computer Science Southwest Petroleum UniversityChengdu610500China

Long-term urban traffic flow prediction is an important task in the field of intelligent transportation,as it can help optimize traffic management and improve travel *** improve prediction accuracy,a crucial issue is how to model spatiotemporal dependency in urban traffic *** recent years,many studies have adopted spatiotemporal neural networks to extract key information from traffic ***,most models ignore the semantic spatial similarity between long-distance areas when mining spatial *** also ignore the impact of predicted time steps on the next unpredicted time step for making long-term ***,these models lack a comprehensive data embedding process to represent complex spatiotemporal *** paper proposes a multi-scale persistent spatiotemporal transformer(MSPSTT)model to perform accurate long-term traffic flow prediction in *** adopts an encoder-decoder structure and incorporates temporal,periodic,and spatial features to fully embed urban traffic data to address these *** model consists of a spatiotemporal encoder and a spatiotemporal decoder,which rely on temporal,geospatial,and semantic space multi-head attention modules to dynamically extract temporal,geospatial,and semantic *** spatiotemporal decoder combines the context information provided by the encoder,integrates the predicted time step information,and is iteratively updated to learn the correlation between different time steps in the broader time range to improve the model’s accuracy for long-term *** on four public transportation datasets demonstrate that MSPSTT outperforms the existing models by up to 9.5%on three common metrics.

关键词： Graph neural network Multi-head attention mechanism Spatio-temporal dependency Traffic flow prediction

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Provably secure authentication protocol for traffic exchanges in unmanned aerial vehicles

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High-Confidence Computing 2023年第4期3卷 60-72页

作者： Vincent Omollo Nyangaresi Department of Computer Science&Software Engineering Jaramogi Oginga Odinga University of Science&TechnologyKenya

Unmanned aerial vehicles offer services such as military reconnaissance in potentially adversarial controlled *** addition,they have been deployed in civilian critical infrastructure *** this environment,real-time and massive data is exchanged between the aerial vehicles and the ground control *** on the mission of these aerial vehicles,some of the collected and transmitted data is sensitive and ***,many security protocols have been presented to offer privacy and security ***,majority of these schemes fail to consider attack vectors such as side-channeling,de-synchronization and known secret session temporary information *** last attack can be launched upon adversarial physical capture of these *** addition,some of these protocols deploy computationally intensive asymmetric cryptographic primitives that result in high *** this paper,an authentication protocol based on lightweight quadratic residues and hash functions is *** formal security analysis is executed using the widely deployed random oracle *** addition,informal security analysis is carried out to show its robustness under the Dolev–Yao(DY)and Canetti–Krawczyk(CK)threat *** terms of operational efficiency,it is shown to have relatively lower execution time,communication costs,and incurs the least storage costs among other related ***,the proposed protocol provides a 25%improvement in supported security and privacy features and a 6.52%reduction in storage *** overall,the proposed methodology offers strong security and privacy protection at lower execution time,storage and communication overheads.

关键词： Authentication Drones Quadratic residues Privacy Security Side-channeling UAV

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VAT: Visibility Aware Transformer for Fine-Grained Clothed Human Reconstruction

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IEEE Transactions on Visualization and computer Graphics 2025年第5期31卷 2277-2287页

作者： Zhang, Xiaoyan Zhu, Zibin Xie, Hong Ren, Sisi Shenzhen University College of Computer Science and Software Engineering Shenzhen518000 China

In order to reconstruct 3D clothed human with accurate fine-grained details from sparse views, we propose a deep cooperating two-level global to fine-grained reconstruction framework that constructs robust global geometry to guide fine-grained geometry learning. The core of the framework is a novel visibility aware Transformer VAT, which bridges the two-level reconstruction architecture by connecting its global encoder and fine-grained decoder with two pixel-aligned implicit functions, respectively. The global encoder fuses semantic features of multiple views to integrate global geometric features. In the fine-grained decoder, visibility aware attention mechanism is designed to efficiently fuse multi-view and multi-scale features for mining fine-grained geometric features. The global encoder and fine-grained decoder are connected by a global embeding module to form a deep cooperation in the two-level framework, which provides global geometric embedding as a query guidance for calculating visibility aware attention in the fine-grained decoder. In addition, to extract highly aligned multi-scale features for the two-level reconstruction architecture, we design an image feature extractor MSUNet, which establishes strong semantic connections between different scales at minimal cost. Our proposed framework is end-to-end trainable, with all modules jointly optimized. We validate the effectiveness of our framework on public benchmarks, and experimental results demonstrate that our method has significant advantages over state-of-the-art methods in terms of both fine-grained performance and generalization. © 2025 IEEE.

关键词： Decoding

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Adaptive ROI Optimization Pyramid Network: Lane Detection for FSD under Data Uncertainty

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engineering Letters 2025年第2期33卷 282-291页

作者： Cao, Xu Liu, Weisheng Wang, Zhijian College of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China College of Computer Science and Software Engineering University of Science and Technology Liaoning Co Anshan114051 China

To mitigate the challenges posed by data uncertainty in Full-Self Driving (FSD) systems. This paper proposes a novel feature extraction learning model called Adaptive Region of Interest Optimized Pyramid Network (ARO). Specifically, ARO introduces a novel cross-layer fusion attention mechanism that dynamically assigns weighted attention to feature maps across different levels, facilitating deep feature fusion and enabling the effective extraction and utilization of salient feature information. Furthermore, ARO incorporates a feature replication layer to duplicate and refine feature maps at multiple levels, thereby enhancing its capability to capture fine-grained details and generate richer feature representations. Additionally, a multi-path upsampling strategy preserves fine-grained features during upsampling. Extensive experimental evaluations conducted on the benchmark CuLane and Tusimple datasets demonstrate that ARO achieves an F1 score of 80.60% on the CuLane dataset, outperforming state-of-the-art methods, showcasing the effectiveness of the proposed approach in handling data uncertainty for robust lane detection in autonomous driving. Our code are available at https://***/caoxu0109/lane detect ldfr. © 2025, International Association of Engineers. All rights reserved.

关键词： Attention mechanisms Culane Lane detection Multi-scale fusion network Tusimple

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Attribute grouping-based naive Bayesian classifier

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science China(Information sciences) 2025年第3期68卷 125-149页

作者： Yulin HE Guiliang OU Philippe FOURNIER-VIGER Joshua Zhexue HUANG Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) College of Computer Science & Software Engineering Shenzhen University

The naive Bayesian classifier(NBC) is a supervised machine learning algorithm having a simple model structure and good theoretical interpretability. However, the generalization performance of NBC is limited to a large extent by the assumption of attribute independence. To address this issue, this paper proposes a novel attribute grouping-based NBC(AG-NBC), which is a variant of the classical NBC trained with different attribute groups. AG-NBC first applies a novel effective objective function to automatically identify optimal dependent attribute groups(DAGs). Condition attributes in the same DAG are strongly dependent on the class attribute, whereas attributes in different DAGs are independent of one another. Then,for each DAG, a random vector functional link network with a SoftMax layer is trained to output posterior probabilities in the form of joint probability density estimation. The NBC is trained using the grouping attributes that correspond to the original condition attributes. Extensive experiments were conducted to validate the rationality, feasibility, and effectiveness of AG-NBC. Our findings showed that the attribute groups chosen for NBC can accurately represent attribute dependencies and reduce overlaps between different posterior probability densities. In addition, the comparative results with NBC, flexible NBC(FNBC), tree augmented Bayes network(TAN), gain ratio-based attribute weighted naive Bayes(GRAWNB), averaged one-dependence estimators(AODE), weighted AODE(WAODE), independent component analysis-based NBC(ICA-NBC), hidden naive Bayesian(HNB) classifier, and correlation-based feature weighting filter for naive Bayes(CFW) show that AG-NBC obtains statistically better testing accuracies, higher area under the receiver operating characteristic curves(AUCs), and fewer probability mean square errors(PMSEs) than other Bayesian classifiers. The experimental results demonstrate that AG-NBC is a valid and efficient approach for alleviating the attribute i

关键词： naive Bayesian classifier attribute independence assumption attribute grouping dependent attribute group posterior probability class-conditional probability

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A Deep Deterministic Policy Gradient-Based Method for Enforcing Service Fault-Tolerance in MEC

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Chinese Journal of Electronics 2024年第4期33卷 899-909页

作者： Tingyan LONG Peng CHEN Yunni XIA Yong MA Xiaoning SUN Jiale ZHAO Yifei LYU College of Computer Science Chongqing University School of Computer and Software Engineering Xihua University School of Computer and Information Engineering Jiangxi Normal University School of Computer and Information Science Chongqing Normal University

Mobile edge computing(MEC) provides edge services to users in a distributed and on-demand *** to the heterogeneity of edge applications, deploying latency and resource-intensive applications on resourceconstrained devices is a key challenge for service providers. This is especially true when underlying edge infrastructures are fault and error-prone. In this paper, we propose a fault tolerance approach named DFGP, for enforcing mobile service fault-tolerance in MEC. It synthesizes a generative optimization network(GON) model for predicting resource failure and a deep deterministic policy gradient(DDPG) model for yielding preemptive migration *** show through extensive simulation experiments that DFGP is more effective in fault detection and guaranteeing quality of service, in terms of fault detection accuracy, migration efficiency, task migration time, task scheduling time,and energy consumption than other existing methods.

关键词： Fault tolerance Multi-access edge computing Processor scheduling Fault detection Image edge detection Fault tolerant systems Quality of service

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LoRaDIP: Low-Rank Adaptation With Deep Image Prior for Generative Low-Light Image Enhancement

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第4期6卷 909-920页

作者： Zhao, Zunjin Shi, Daming Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

This article presents LoRaDIP, a novel low-light image enhancement (LLIE) model based on deep image priors (DIPs). While DIP-based enhancement models are known for their zero-shot learning, their expensive computational cost remains a challenge. In addressing this issue, our proposed LoRaDIP introduces a low-rank adaptation technique, significantly reducing computational expenses without compromising performance. The contributions of this work are threefold. First, we eliminate the need for estimating initial illumination and reflectance, opting instead to directly estimate the illumination map from the observed image in a generative fashion. The illumination is parameterized by a DIP network. Second, considering the overparameterization of DIP networks, we introduce a low-rank adaptation technique to decrease the number of trainable parameters, thereby reducing computational demands. Third, differing from the existing DIP-based models that rely on a preset fixed number of iterations to halt the optimization process of Retinex decomposition, we propose an automatic stopping criterion based on stable rank, preventing unnecessary iterations. LoRaDIP not only inherits the advantage of requiring only the single input image but also exhibits reduced computational costs while maintaining or even surpassing the performance of state-of-the-art models. © 2020 IEEE.

关键词： Zero-shot learning

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