标题： 轻量级多模态多视图卷积-视觉Transformer方法用于3D物体识别：LM-MCVT 显示英文标题

标题： LM-MCVT: A Lightweight Multi-modal Multi-view Convolutional-Vision Transformer Approach for 3D Object Recognition

摘要： 在以人类为中心的环境中，如餐厅、家庭和仓库，机器人常常难以准确识别三维物体。 这些挑战源于这些环境的复杂性和变异性，包括物体形状的多样性。 本文提出了一种新颖的轻量级多模态多视图卷积-视觉Transformer网络（LM-MCVT），以增强机器人应用中的三维物体识别。 我们的方法利用基于全局熵的嵌入融合（GEEF）方法高效地整合多视图信息。 LM-MCVT架构结合了预处理和中级卷积编码器以及局部和全局Transformer，以提升特征提取和识别准确性。 我们在合成的ModelNet40数据集上评估了我们的方法，在四视图设置下达到了95.6%的识别准确率，超过了现有的最先进方法。 为了进一步验证其有效性，我们使用相同的配置在现实世界的OmniObject3D数据集上进行了5折交叉验证。 结果始终显示出优越的性能，证明了该方法在合成和真实世界三维数据中的三维物体识别的鲁棒性。 显示英文摘要

摘要： In human-centered environments such as restaurants, homes, and warehouses, robots often face challenges in accurately recognizing 3D objects. These challenges stem from the complexity and variability of these environments, including diverse object shapes. In this paper, we propose a novel Lightweight Multi-modal Multi-view Convolutional-Vision Transformer network (LM-MCVT) to enhance 3D object recognition in robotic applications. Our approach leverages the Globally Entropy-based Embeddings Fusion (GEEF) method to integrate multi-views efficiently. The LM-MCVT architecture incorporates pre- and mid-level convolutional encoders and local and global transformers to enhance feature extraction and recognition accuracy. We evaluate our method on the synthetic ModelNet40 dataset and achieve a recognition accuracy of 95.6% using a four-view setup, surpassing existing state-of-the-art methods. To further validate its effectiveness, we conduct 5-fold cross-validation on the real-world OmniObject3D dataset using the same configuration. Results consistently show superior performance, demonstrating the method's robustness in 3D object recognition across synthetic and real-world 3D data.