PAVLM: Advancing Point Cloud based Affordance Understanding Via Vision-Language Model

• URL: http://arxiv.org/abs/2410.11564v1
• Date: Tue, 15 Oct 2024 12:53:42 GMT
• Title: PAVLM: Advancing Point Cloud based Affordance Understanding Via Vision-Language Model
• Authors: Shang-Ching Liu, Van Nhiem Tran, Wenkai Chen, Wei-Lun Cheng, Yen-Lin Huang, I-Bin Liao, Yung-Hui Li, Jianwei Zhang,
• Abstract summary: Affordance understanding, the task of identifying actionable regions on 3D objects, plays a vital role in allowing robotic systems to engage with and operate within the physical world. Visual Language Models (VLMs) have excelled in high-level reasoning but fall short in grasping the nuanced physical properties required for effective human-robot interaction. We introduce PAVLM, an innovative framework that utilizes the extensive multimodal knowledge embedded in pre-trained language models to enhance 3D affordance understanding of point cloud.
• Score: 4.079327215055764
• License:
• Abstract: Affordance understanding, the task of identifying actionable regions on 3D objects, plays a vital role in allowing robotic systems to engage with and operate within the physical world. Although Visual Language Models (VLMs) have excelled in high-level reasoning and long-horizon planning for robotic manipulation, they still fall short in grasping the nuanced physical properties required for effective human-robot interaction. In this paper, we introduce PAVLM (Point cloud Affordance Vision-Language Model), an innovative framework that utilizes the extensive multimodal knowledge embedded in pre-trained language models to enhance 3D affordance understanding of point cloud. PAVLM integrates a geometric-guided propagation module with hidden embeddings from large language models (LLMs) to enrich visual semantics. On the language side, we prompt Llama-3.1 models to generate refined context-aware text, augmenting the instructional input with deeper semantic cues. Experimental results on the 3D-AffordanceNet benchmark demonstrate that PAVLM outperforms baseline methods for both full and partial point clouds, particularly excelling in its generalization to novel open-world affordance tasks of 3D objects. For more information, visit our project site: pavlm-source.github.io.

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