# Open-Vocabulary Affordance Detection using Knowledge Distillation and Text-Point Correlation

arXiv: https://arxiv.org/abs/2309.10932 · HTML: https://mlsanity.com/paper/2309.10932/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2023.

## Built on by (most cited first, 18 of 18)

- [GREAT: Geometry-Intention Collaborative Inference for Open-Vocabulary 3D Object Affordance Grounding](https://mlsanity.com/paper/2411.19626/) (2024)
- [Language-Driven 6-DoF Grasp Detection Using Negative Prompt Guidance](https://mlsanity.com/paper/2407.13842/) (2024)
- [WorldAfford: Affordance Grounding based on Natural Language Instructions](https://mlsanity.com/paper/2405.12461/) (2024)
- [LLM-enhanced Scene Graph Learning for Household Rearrangement](https://mlsanity.com/paper/2408.12093/) (2024)
- [Task-Aware 3D Affordance Segmentation via 2D Guidance and Geometric Refinement](https://mlsanity.com/paper/2511.11702/) (2025)
- [Robotic-CLIP: Fine-tuning CLIP on Action Data for Robotic Applications](https://mlsanity.com/paper/2409.17727/) (2024)
- [Afford-X: Generalizable and Slim Affordance Reasoning for Task-oriented Manipulation](https://mlsanity.com/paper/2503.03556/) (2025)
- [Language-driven Grasp Detection with Mask-guided Attention](https://mlsanity.com/paper/2407.19877/) (2024)
- [Unlocking 3D Affordance Segmentation with 2D Semantic Knowledge](https://mlsanity.com/paper/2510.08316/) (2025)
- [HAMMER: Harnessing MLLM via Cross-Modal Integration for Intention-Driven 3D Affordance Grounding](https://mlsanity.com/paper/2603.02329/) (2026)
- [DAP: Diffusion-based Affordance Prediction for Multi-modality Storage](https://mlsanity.com/paper/2409.00499/) (2024)
- [Capturing Fine-Grained Alignments Improves 3D Affordance Detection](https://mlsanity.com/paper/2506.19312/) (2025)
- [CRAFT-E: A Neuro-Symbolic Framework for Embodied Affordance Grounding](https://mlsanity.com/paper/2512.04231/) (2025)
- [Probing and Bridging Geometry-Interaction Cues for Affordance Reasoning in Vision Foundation Models](https://mlsanity.com/paper/2602.20501/) (2026)
- [AffordMatcher: Affordance Learning in 3D Scenes from Visual Signifiers](https://mlsanity.com/paper/2603.27970/) (2026)
- [VoxAfford: Multi-Scale Voxel-Token Fusion for Open-Vocabulary 3D Affordance Detection](https://mlsanity.com/paper/2605.01365/) (2026)
- [Zero-shot 2D Grounding with Novel Affordance Types](https://mlsanity.com/paper/2608.08929/) (2026)
- [UniAfford: Token-Routed Multitask Learning for Generalizable 2D-3D Affordance Perception](https://mlsanity.com/paper/2609.37264/) (2026)

## Abstract (arXiv)

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.