基于知识蒸馏与文本-点云关联的开放词汇可供性检测
Open-Vocabulary Affordance Detection using Knowledge Distillation and Text-Point Correlation
AI总结：
本文提出一种基于知识蒸馏和文本-点云关联的三维点云开放词汇可供性检测方法，通过预训练模型增强特征提取并学习点云与标签的语义关联，在mIOU上提升7.96%，支持实时推理并适用于机器人操作。
摘要：
可供性检测面临复杂挑战，并在机器人应用中具有广泛用途。以往工作受到三维物体形状复杂性、真实物体上潜在可供性范围广泛以及可供性理解缺乏开放词汇支持等限制。本文提出一种新的三维点云开放词汇可供性检测方法，利用知识蒸馏和文本-点云关联。我们的方法通过知识蒸馏使用预训练三维模型，以增强三维点云中的特征提取和语义理解。我们进一步提出一种新的文本-点云关联方法，用于学习点云特征与开放词汇标签之间的语义联系。大量实验表明，我们的方法优于以往工作，并能适应新的可供性标签和未见过的物体。值得注意的是，与基线相比，我们的方法在mIOU分数上实现了7.96%的提升。此外，该方法支持实时推理，非常适合机器人操作应用。
英文摘要：
Affordance detection presents intricate challenges and has a wide range of robotic applications. Previous works have faced limitations such as the complexities of 3D object shapes, the wide range of potential affordances on real-world objects, and the lack of open-vocabulary support for affordance understanding. In this paper, we introduce a new open-vocabulary affordance detection method in 3D point clouds, leveraging knowledge distillation and text-point correlation. Our approach employs pre-trained 3D models through knowledge distillation to enhance feature extraction and semantic understanding in 3D point clouds. We further introduce a new text-point correlation method to learn the semantic links between point cloud features and open-vocabulary labels. The intensive experiments show that our approach outperforms previous works and adapts to new affordance labels and unseen objects. Notably, our method achieves the improvement of 7.96% mIOU score compared to the baselines. Furthermore, it offers real-time inference which is well-suitable for robotic manipulation applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
