重庆理工大学学报2025，Vol.39Issue(1)：125-131,7.DOI:10.3969/j.issn.1674-8425(z).2025.01.016
多模态特征融合的三维形状识别网络
3D shape recognition network based on multimodal feature fusion
摘要
Abstract
The global feature fusion of point cloud and view modalities has been proven effective.To further explore fine-grained local feature relationships and complementary relationships between modalities and improve the performance of the model,we propose a new network architecture,which consists of two core modules.First,a Local Feature Fusion Module(LFM)is designed,integrating the local features of two modalities at different levels through transposed changes in the feature matrix.The feature complementarity enhancement module utilizes element level simple operations to obtain discriminative information between modalities and employs them as a basis to quantify weight coefficients.Finally,the feature is weighted and enhanced to form stronger shape descriptors.A large number of experiments conducted on the dataset ModelNet10/40 show the network reaches a balance between efficiency and performance and dilivers superior performances in 3D shape recognition.关键词
多模态/三维形状理解/深度学习/局部特征Key words
multimodal/understanding 3D shapes/deep learning/local feature分类
信息技术与安全科学引用本文复制引用
但远宏,王志浩,金毓..多模态特征融合的三维形状识别网络[J].重庆理工大学学报,2025,39(1):125-131,7.基金项目
重庆市科委重点攻关计划项目(2021CCB03) （2021CCB03）
南京理工大学重点实验室基金赞助项目(2022-JCJQ-LB-061-07) （2022-JCJQ-LB-061-07）
