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.Comment: 8 page