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学者姓名：罗欢
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Traditional object classification in 3D point cloud scenes relies heavily on large-scale labeled training data, which is both time-consuming and labor-intensive to obtain. Unsupervised Domain Transfer (UDT) mitigates this challenge by transferring knowledge from a labeled source domain to an unlabeled target domain. However, existing UDT-based methods often require complex neural architectures and substantial computational resources. This letter proposes a novel UDT framework that integrates hierarchical prompt learning with a 3D foundational model. The proposed method consists of a modality alignment stage and an unsupervised transfer stage. In the modality alignment stage, cross-modal hierarchical prompts are employed to align the Visual-Language (V-L) modality in the 3D foundational model through a V-L coupling module. In the unsupervised transfer stage, cross-domain hierarchical prompts and a Target-Source (T-S) coupling module facilitate the alignment of multi-scale contextual information across domains, ensuring efficient and accurate knowledge transfer. Extensive experiments conducted on multiple datasets collected from various laser scanners demonstrate the effectiveness of our proposed approach. © 1994-2012 IEEE.
Keyword ：
3D Object Classification 3D Object Classification 3D Point Clouds 3D Point Clouds Prompt Learning Prompt Learning Unsupervised Domain Transfer Unsupervised Domain Transfer
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| GB/T 7714 | Luo, H. , Fu, K. , Fang, L. . Unsupervised Domain Transfer for Object Classification in 3D Point Clouds via Hierarchical Prompt Learning [J]. | IEEE Signal Processing Letters , 2025 . |
| MLA | Luo, H. 等. "Unsupervised Domain Transfer for Object Classification in 3D Point Clouds via Hierarchical Prompt Learning" . | IEEE Signal Processing Letters (2025) . |
| APA | Luo, H. , Fu, K. , Fang, L. . Unsupervised Domain Transfer for Object Classification in 3D Point Clouds via Hierarchical Prompt Learning . | IEEE Signal Processing Letters , 2025 . |
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Traffic Engineering (TE) is an efficient technique to balance network flows and thus improves the performance of a hybrid Software Defined Network (SDN). Previous TE solutions mainly leverage heuristic algorithms to centrally optimize link weight setting or traffic splitting ratios under the static traffic demand. Note that as the network scale becomes larger and network management gains more complexity, it is notably that the centralized TE methods suffer from a high computation overhead and a long reaction time to optimize routing of flows when the network traffic demand dynamically fluctuates or network failures happen. To enable adaptive and efficient routing in distributed TE, we propose a Multi-agent Reinforcement Learning method CMRL that divides the routing optimization of a large network into multiple small-scale routing decision-making problems. To coordinate the multiple agents for achieving a global optimization goal in a hybrid SDN scenario, we construct a reasonable virtual environment to meet different routing constraints brought by legacy routers and SDN switches for training the routing agents. To train the routing agents for determining the local routing policies according to local network observations, we introduce the difference reward assignment mechanism for encouraging agents to cooperatively take optimal routing action. Extensive simulations conducted on the real traffic traces demonstrate the superiority of CMRL in improving TE performance, especially when traffic demands change or network failures happen.
Keyword ：
Distributed traffic engineering Distributed traffic engineering imitation learning imitation learning network-wide guidance network-wide guidance reinforcement learning reinforcement learning transformer transformer
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| GB/T 7714 | Guo, Yingya , Lin, Bin , Tang, Qi et al. Distributed Traffic Engineering in Hybrid Software Defined Networks: A Multi-Agent Reinforcement Learning Framework [J]. | IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT , 2024 , 21 (6) : 6759-6769 . |
| MLA | Guo, Yingya et al. "Distributed Traffic Engineering in Hybrid Software Defined Networks: A Multi-Agent Reinforcement Learning Framework" . | IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 21 . 6 (2024) : 6759-6769 . |
| APA | Guo, Yingya , Lin, Bin , Tang, Qi , Ma, Yulong , Luo, Huan , Tian, Han et al. Distributed Traffic Engineering in Hybrid Software Defined Networks: A Multi-Agent Reinforcement Learning Framework . | IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT , 2024 , 21 (6) , 6759-6769 . |
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Hybrid Software Defined Networks (Hybrid SDNs), with a partial upgrade of legacy routers to SDN switches in traditional distributed networks, currently stand as a prevailing network architecture. Traffic Engineering (TE) in hybrid SDN requires the efficient and timely acquisition of a routing policy to adapt to dynamically changing traffic demands, which has recently become a hot topic. Ignoring the hidden relations of consecutive states and the compact representation of the network environment, previous Deep Reinforcement Learning (DRL)-based studies suffer from the convergence problem by only establishing the direct relationship between individual Traffic Matrix (TM) and routing policy. Therefore, to enhance TE performance in hybrid SDNs under dynamically changing traffic demands, we propose to integrate the Transformer model with DRL to establish the relationship between consecutive states and routing policies. The temporal characteristic among consecutive states can effectively assist DRL in solving the convergence problem. To obtain a compact and accurate description of the network environment, we propose to jointly consider TM, routing action, and reward in designing the state of the network environment. To better capture the temporal relations among consecutive states of the network environment, we design a multi-feature embedding module and achieve positional encodings in the Transformer model. The extensive experiments demonstrate that once the convergence problem is solved, the proposed Transformer-based DRL method can efficiently generate routing policies that adapt well to dynamic network traffic.
Keyword ：
Deep Reinforcement Learning Deep Reinforcement Learning Dynamic traffic Dynamic traffic Hybrid SDN Hybrid SDN Traffic Engineering Traffic Engineering Transformer Transformer
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| GB/T 7714 | Lin, Bin , Guo, Yingya , Luo, Huan et al. TITE: A transformer-based deep reinforcement learning approach for traffic engineering in hybrid SDN with dynamic traffic [J]. | FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE , 2024 , 161 : 95-105 . |
| MLA | Lin, Bin et al. "TITE: A transformer-based deep reinforcement learning approach for traffic engineering in hybrid SDN with dynamic traffic" . | FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 161 (2024) : 95-105 . |
| APA | Lin, Bin , Guo, Yingya , Luo, Huan , Ding, Mingjie . TITE: A transformer-based deep reinforcement learning approach for traffic engineering in hybrid SDN with dynamic traffic . | FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE , 2024 , 161 , 95-105 . |
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Hybrid Software Defined Networks (Hybrid SDNs), which combines the robustness of distributed network and the flexibility of centralized network, is now a prevailing network architecture. Previous hybrid SDN Traffic Engineering (TE) solutions search an optimal link weight setting or compute the splitting ratios of traffic leveraging heuristic algorithms. However, these methods cannot react timely to the fluctuating traffic demands in dynamic environments and suffer a hefty performance degradation when traffic demands change or network failures happen, especially when network scale is large. To cope with this, we propose a Multi- Agent reinforcement learning based TE method MATE that timely determines the route selection for network flows in dynamic hybrid SDNs. Through dividing the large-scale routing optimization problem into small-scale problem, MATE can better learn the mapping between the traffic demands and routing policy, and efficiently make online routing inference with dynamic traffic demands. To collaborate multiple agents and speed up the convergence in the training process, we innovatively design the actor network and introduce previous actions of all agents in the training of each agent. Extensive experiments conducted on different network topologies demonstrate our proposed method MATE has superior TE performance with dynamic traffic demands and is robust to network failures.
Keyword ：
Dynamic environment Dynamic environment Hybrid Software Defined Networks Hybrid Software Defined Networks Multi-agent reinforcement learning Multi-agent reinforcement learning Traffic Engineering Traffic Engineering
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| GB/T 7714 | Guo, Yingya , Ding, Mingjie , Zhou, Weihong et al. MATE: A multi-agent reinforcement learning approach for Traffic Engineering in Hybrid Software Defined Networks [J]. | JOURNAL OF NETWORK AND COMPUTER APPLICATIONS , 2024 , 231 . |
| MLA | Guo, Yingya et al. "MATE: A multi-agent reinforcement learning approach for Traffic Engineering in Hybrid Software Defined Networks" . | JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 231 (2024) . |
| APA | Guo, Yingya , Ding, Mingjie , Zhou, Weihong , Lin, Bin , Chen, Cen , Luo, Huan . MATE: A multi-agent reinforcement learning approach for Traffic Engineering in Hybrid Software Defined Networks . | JOURNAL OF NETWORK AND COMPUTER APPLICATIONS , 2024 , 231 . |
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Network failures, especially link failures, happen frequently in Internet Service Provider (ISP) networks. When link failures occur, the routing policies need to be re-computed and failure recovery usually takes a few minutes, which degrades the network performance to a great extent. Therefore, a proper failure recovery scheme that can realize a fast and timely routing policy computation needs to be designed. In this paper, we propose FRRL, a Reinforcement Learning (RL) approach to intelligently perceive network failures and timely compute the routing policy for improving the network performance when link failure happens. Specifically, to perceive the link failures, we design a Topology Difference Vector (TDV) encoder module in FRRL for encoding the topology structure with link failures. To efficiently compute the routing policy when link failures happen, we integrate the TDV in the agent training for learning the map between the encoded failure topology structure and routing policies. To evaluate the performance of our proposed method, we conduct experiments on three network topologies and the experimental results demonstrate that our proposed method has superior performance when link failures happen compared to other methods.
Keyword ：
Link failure recovery Link failure recovery Reinforcement learning Reinforcement learning Routing optimization Routing optimization Traffic engineering Traffic engineering
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| GB/T 7714 | Ma, Yulong , Guo, Yingya , Yang, Ruiyu et al. FRRL: A reinforcement learning approach for link failure recovery in a hybrid SDN [J]. | JOURNAL OF NETWORK AND COMPUTER APPLICATIONS , 2024 , 234 . |
| MLA | Ma, Yulong et al. "FRRL: A reinforcement learning approach for link failure recovery in a hybrid SDN" . | JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 234 (2024) . |
| APA | Ma, Yulong , Guo, Yingya , Yang, Ruiyu , Luo, Huan . FRRL: A reinforcement learning approach for link failure recovery in a hybrid SDN . | JOURNAL OF NETWORK AND COMPUTER APPLICATIONS , 2024 , 234 . |
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Three-dimensional (3D) point cloud data have been widely used in many fields, such as autonomous driving, robotics, and high-precision mapping. At present, the state-of-the-art deep learning-based methods for 3D point cloud processing are mainly supervised learning methods. The performance of these methods depends heavily on large-scale, high-quality annotated datasets. However, annotating a large-scale, high-quality, category-diverse, and scenario-rich dataset is time-consuming and labor-intensive. In particular, obtaining sufficiently large numbers of samples for model optimization is also quite difficult in some special cases. In addition, 3D point cloud processing models trained on a single device in a special environment are difficult to generalize to different devices and environments. Their generalizability to various devices and environments is limited. Thus, how to reduce dependencies on high-quality annotated 3D point cloud datasets and how to improve the generalizability of current point cloud processing models are important research topics. In recent years, various kinds of impressive and elaborate technologies, such as meta-learning, few-shot learning, transfer learning, self-supervised learning, semisupervised learning, and weakly supervised learning, have been proposed to solve this problem. As an important research branch of transfer learning, domain adaptive learning aims to eliminate differences in feature distributions across domains and promote the generalization ability of deep learning models, thereby providing a novel solution to address this problem effectively. The academic community has conducted preliminary research on domain adaptive learning for point cloud processing. However, the domain adaptive learning field for point clouds still requires in-depth and effective exploration. Consequently, this study systematically summarizes and classifies recent 3D point cloud domain adaptive learning methods into five categories: adversarial learning, cross-modal learning, pseudo-label learning, data alignment, and other kinds of methods. First, we present the mathematical definition of the domain adaptive learning task and depict the chronological overview of the development of different domain adaptive learning methods to provide readers with a clear understanding. Second, we present the general solution for each category of domain adaptive learning methods and summarize the advantages and disadvantages of the current methods for each category. Third, we compare the performance of current methods on three-point cloud processing tasks, including 3D shape classification, 3D object detection, and 3D semantic segmentation. For each task, we also summarize the commonly used datasets and evaluation metrics for an intuitional comparison. Finally, we conclude the advantages and disadvantages of these five categories of methods and discuss future research directions about the 3D point cloud domain adaptive learning. © 2024 Science Press. All rights reserved.
Keyword ：
3D modeling 3D modeling Deep learning Deep learning Large datasets Large datasets Learning systems Learning systems Remote sensing Remote sensing Supervised learning Supervised learning Transfer learning Transfer learning
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| GB/T 7714 | Fan, Wenhui , Lin, Xi , Luo, Huan et al. Domain adaptation learning for 3D point clouds：A survey [J]. | National Remote Sensing Bulletin , 2024 , 28 (4) : 825-842 . |
| MLA | Fan, Wenhui et al. "Domain adaptation learning for 3D point clouds：A survey" . | National Remote Sensing Bulletin 28 . 4 (2024) : 825-842 . |
| APA | Fan, Wenhui , Lin, Xi , Luo, Huan , Guo, Wenzhong , Wang, Hanyun , Dai, Chenguang . Domain adaptation learning for 3D point clouds：A survey . | National Remote Sensing Bulletin , 2024 , 28 (4) , 825-842 . |
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三维点云数据在自动驾驶、机器人和高精地图等领域得到了广泛应用。目前，基于深度学习的三维点云数据处理主要基于有监督学习，其算法性能依赖于大规模高质量的标注数据集。此外，仅在单一设备与场景中训练的三维点云数据处理模型难以应用于不同设备与环境，泛化性能有限。因此，如何减少三维点云标注数据集的需求以及提高三维点云处理模型的适应性是当前三维点云数据处理面临的重要难题。作为迁移学习的一个重要分支，域自适应学习旨在不同域间特征分布存在差异的情况下提高模型的适应性，可为解决上述难题提供重要思路。为便于对点云域自适应学习领域进行更深入有效的探索，本文主要从对抗学习、跨模态学习、伪标签学习、数据对齐及其他方法 5个方面对近年来的三维点云域自适应学习方法进行了系统梳理与分类归纳，并分析总结每类点云域自适应学习方法所具备的优势及面临的问题。最后，对三维点云域自适应学习研究领域的未来发展进行了展望。
Keyword ：
三维点云 三维点云 伪标签学习 伪标签学习 域自适应学习 域自适应学习 对抗学习 对抗学习 数据对齐 数据对齐 跨模态学习 跨模态学习 遥感 遥感
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| GB/T 7714 | 范文辉 , 林茜 , 罗欢 et al. 面向三维点云的域自适应学习 [J]. | 遥感学报 , 2024 , 28 (04) : 825-842 . |
| MLA | 范文辉 et al. "面向三维点云的域自适应学习" . | 遥感学报 28 . 04 (2024) : 825-842 . |
| APA | 范文辉 , 林茜 , 罗欢 , 郭文忠 , 汪汉云 , 戴晨光 . 面向三维点云的域自适应学习 . | 遥感学报 , 2024 , 28 (04) , 825-842 . |
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Routing optimization, as a significant part of Traffic Engineering (TE), plays an important role in balancing network traffic and improving quality of service. With the application of Machine Learning (ML) in various fields, many neural network-based routing optimization solutions have been proposed. However, most existing ML-based methods need to retrain the model when confronted with a network unseen during training, which incurs significant time overhead and response delay. To improve the generalization ability of the routing model, in this paper, we innovatively propose a routing optimization method GROM which combines Deep Reinforcement Learning (DRL) and Graph Neural Networks (GNN), to directly generate routing policies under different and unseen network topologies without retraining. Specifically, for handling different network topologies, we transform the traffic-splitting ratio into element -level output of GNN model. To make the DRL agent easier to converge and well generalize to unseen topologies, we discretize the huge continuous trafficsplitting action space. Extensive simulation results on five real-world network topologies demonstrate that GROM can rapidly generate routing policies under different network topologies and has superior generalization ability.
Keyword ：
Graph neural networks Graph neural networks Reinforcement learning Reinforcement learning Software-defined networks Software-defined networks Traffic engineering Traffic engineering
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| GB/T 7714 | Ding, Mingjie , Guo, Yingya , Huang, Zebo et al. GROM: A generalized routing optimization method with graph neural network and deep reinforcement learning [J]. | JOURNAL OF NETWORK AND COMPUTER APPLICATIONS , 2024 , 229 . |
| MLA | Ding, Mingjie et al. "GROM: A generalized routing optimization method with graph neural network and deep reinforcement learning" . | JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 229 (2024) . |
| APA | Ding, Mingjie , Guo, Yingya , Huang, Zebo , Lin, Bin , Luo, Huan . GROM: A generalized routing optimization method with graph neural network and deep reinforcement learning . | JOURNAL OF NETWORK AND COMPUTER APPLICATIONS , 2024 , 229 . |
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With the continuous development of intelligent transportation technologies such as autonomous driving and navigation, accurate perception of road markings becomes crucial. However, due to limitations in sensor perspectives and obstacles blocking the view, the scanned point cloud of road markings is often incomplete, potentially leading to erroneous decisions in intelligent transportation systems. Therefore, it becomes imperative to recover complete road markings from these incomplete point clouds. This paper proposes a text-guided road marking completion method, which integrates text information with point cloud data using attention mechanisms. By leveraging text information to guide the network in completing road marking point clouds, the proposed method aims to enhance the perception accuracy and completeness of road markings. Experimental validation on road marking datasets demonstrates the effectiveness and feasibility of the proposed approach. © 2024 IEEE.
Keyword ：
Intelligent systems Intelligent systems Road and street markings Road and street markings
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| GB/T 7714 | He, Xu , Xu, Qihong , Luo, Huan . Text Guided Road Marking Point Cloud Completion [C] . 2024 : 468-471 . |
| MLA | He, Xu et al. "Text Guided Road Marking Point Cloud Completion" . (2024) : 468-471 . |
| APA | He, Xu , Xu, Qihong , Luo, Huan . Text Guided Road Marking Point Cloud Completion . (2024) : 468-471 . |
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Three-dimensional semantic understanding using only several multi-view images can largely reduce the communication burden on the network. In addition, while point clouds are extensively studied for 3D scene understanding, utilization of multi-view image data offers rich visual details and texture information. However, challenges persist in lifting 2D semantic features to 3D space and leveraging language for segmentation. Inspired by recent advancements, this paper proposes a method that combines CLIP features and SAM masks to create a feature field capable of segmenting objects via natural language text across 2D multi-view and 3D Gaussian splatting. It offers a promising function for extracting 3D assets for game engines and the metaverse. Our method involves mask generation from video frames, extracting physical scales via RGB Nerf with masks, and organizing hierarchical information for semantic comprehension. In the training process, affinity features maintain scale properties and guide CLIP feature generation with auto weights blending for semantic robustness. A straightforward 3D splatting CLIP feature approach and canonical text methodology enhance query robustness across 2D multi-view and 3D splatting through relevance score calculation based on text CLIP features for inference. Experimental results demonstrate promising improvements in semantic understanding of 3D scenes. © 2024 IEEE.
Keyword ：
Image texture Image texture Natural language processing systems Natural language processing systems Query languages Query languages Semantics Semantics Semantic Segmentation Semantic Segmentation
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| GB/T 7714 | Fan, Yujie , Luo, Huan . Open-vocabulary 3D Semantic Understanding via Affinity Neural Radiance Fields [C] . 2024 : 464-467 . |
| MLA | Fan, Yujie et al. "Open-vocabulary 3D Semantic Understanding via Affinity Neural Radiance Fields" . (2024) : 464-467 . |
| APA | Fan, Yujie , Luo, Huan . Open-vocabulary 3D Semantic Understanding via Affinity Neural Radiance Fields . (2024) : 464-467 . |
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