Recently,large-scale deep learning models have been increasingly adopted for point cloud classification.However,thesemethods typically require collecting extensive datasets frommultiple clients,which may lead to priva...Recently,large-scale deep learning models have been increasingly adopted for point cloud classification.However,thesemethods typically require collecting extensive datasets frommultiple clients,which may lead to privacy leaks.Federated learning provides an effective solution to data leakage by eliminating the need for data transmission,relying instead on the exchange of model parameters.However,the uneven distribution of client data can still affect the model’s ability to generalize effectively.To address these challenges,we propose a new framework for point cloud classification called Federated Dynamic Aggregation Selection Strategy-based Multi-Receptive Field Fusion Classification Framework(FDASS-MRFCF).Specifically,we tackle these challenges with two key innovations:(1)During the client local training phase,we propose a Multi-Receptive Field Fusion Classification Model(MRFCM),which captures local and global structures in point cloud data through dynamic convolution and multi-scale feature fusion,enhancing the robustness of point cloud classification.(2)In the server aggregation phase,we introduce a Federated Dynamic Aggregation Selection Strategy(FDASS),which employs a hybrid strategy to average client model parameters,skip aggregation,or reallocate local models to different clients,thereby balancing global consistency and local diversity.We evaluate our framework using the ModelNet40 and ShapeNetPart benchmarks,demonstrating its effectiveness.The proposed method is expected to significantly advance the field of point cloud classification in a secure environment.展开更多
点云语义分割作为三维场景理解的重要任务之一,在智慧城市、智能化测绘等领域具有重要的应用价值。然而,现有分割网络在应对复杂城市场景时,易出现空间关系建模不准确、多尺度语义提取不充分等问题。因此,提出一种融合空间感知与多尺度...点云语义分割作为三维场景理解的重要任务之一,在智慧城市、智能化测绘等领域具有重要的应用价值。然而,现有分割网络在应对复杂城市场景时,易出现空间关系建模不准确、多尺度语义提取不充分等问题。因此,提出一种融合空间感知与多尺度特征的城市级点云语义分割方法LoGNet(local and global network)。通过联合编码点云的几何坐标、颜色属性与上下文语义关系,提升对地物形态差异、光谱特征与空间关联的表达能力;将可学习的空间距离权重与语义相似度共同引入邻域建模,实现基于结构特征与外观属性的精细聚合;构建轻量级的局部–全局双路径特征融合框架,通过点维度与通道维度的全局特征生成方式,强化跨尺度语义一致性与边界解析能力。在Toronto-3D、SensatUrban、STPLS3D公开数据集,与已有常用方法的比较实验表明:LoGNet在三个公开数据集的总体精度分别达97.5%、94.3%、75.0%,均表现最优;在SensatUrban数据集,相较于基线模型,LoGNet的OA、m IoU分别提升了4.5%、10%,在建筑、铁轨、马路等中大型结构性类别,取得了最高得分;对识别极小目标类别,也有显著优势。展开更多
基金supported in part by the National Key Research and Development Program of Chinaunder(Grant 2021YFB3101100)in part by the National Natural Science Foundation of Chinaunder(Grant 42461057),(Grant 62272123),and(Grant 42371470)+1 种基金in part by the Fundamental Research Program of Shanxi Province under(Grant 202303021212164)in part by the Postgraduate Education Innovation Program of Shanxi Province under(Grant 2024KY474).
文摘Recently,large-scale deep learning models have been increasingly adopted for point cloud classification.However,thesemethods typically require collecting extensive datasets frommultiple clients,which may lead to privacy leaks.Federated learning provides an effective solution to data leakage by eliminating the need for data transmission,relying instead on the exchange of model parameters.However,the uneven distribution of client data can still affect the model’s ability to generalize effectively.To address these challenges,we propose a new framework for point cloud classification called Federated Dynamic Aggregation Selection Strategy-based Multi-Receptive Field Fusion Classification Framework(FDASS-MRFCF).Specifically,we tackle these challenges with two key innovations:(1)During the client local training phase,we propose a Multi-Receptive Field Fusion Classification Model(MRFCM),which captures local and global structures in point cloud data through dynamic convolution and multi-scale feature fusion,enhancing the robustness of point cloud classification.(2)In the server aggregation phase,we introduce a Federated Dynamic Aggregation Selection Strategy(FDASS),which employs a hybrid strategy to average client model parameters,skip aggregation,or reallocate local models to different clients,thereby balancing global consistency and local diversity.We evaluate our framework using the ModelNet40 and ShapeNetPart benchmarks,demonstrating its effectiveness.The proposed method is expected to significantly advance the field of point cloud classification in a secure environment.
文摘点云语义分割作为三维场景理解的重要任务之一,在智慧城市、智能化测绘等领域具有重要的应用价值。然而,现有分割网络在应对复杂城市场景时,易出现空间关系建模不准确、多尺度语义提取不充分等问题。因此,提出一种融合空间感知与多尺度特征的城市级点云语义分割方法LoGNet(local and global network)。通过联合编码点云的几何坐标、颜色属性与上下文语义关系,提升对地物形态差异、光谱特征与空间关联的表达能力;将可学习的空间距离权重与语义相似度共同引入邻域建模,实现基于结构特征与外观属性的精细聚合;构建轻量级的局部–全局双路径特征融合框架,通过点维度与通道维度的全局特征生成方式,强化跨尺度语义一致性与边界解析能力。在Toronto-3D、SensatUrban、STPLS3D公开数据集,与已有常用方法的比较实验表明:LoGNet在三个公开数据集的总体精度分别达97.5%、94.3%、75.0%,均表现最优;在SensatUrban数据集,相较于基线模型,LoGNet的OA、m IoU分别提升了4.5%、10%,在建筑、铁轨、马路等中大型结构性类别,取得了最高得分;对识别极小目标类别,也有显著优势。
