基于多模态融合的三维模型检索算法研究

为了获得更好的三维模型检索分类性能,基于深度学习模型研究了多模态信息融合对三维模型的特征描述,在训练步骤提出相关性损失函数来指导不同模态之间的训练,提取更稳健的特征向量;最后将融合特征应用于三维模型的检索和分类,在ModelNet40数据集上进行了三维模型分类任务和检索任务评估。实验结果及与现有方法进行的对比证明了该方法的优越性,为三维模型检索分类领域提供了一种新的思路。

3D model retrieval algorithm based on multimodal fusion

In order to get better performance of 3D model retrieval and classification,this paper proposed the characterization of 3D models by multi-modal information fusion,which based on deep-learning model.The method considered the correlation of different modalities in the training step for extracting a more robust feature vector,which benefitted from the proposed correlation loss function.In addition,it applied fusion features to 3D model retrieval and classification.This paper evaluated the proposed method on the ModelNet40 dataset for 3D models classification task and retrieval.The comparison between the experimental results and the existing methods proves the superiority of this method,which provides a new idea for the field of 3D model retrieval and classification.