GREAT：面向开放词汇3D物体可供性定位的几何-意图协同推理
GREAT: Geometry-Intention Collaborative Inference for Open-Vocabulary 3D Object Affordance Grounding
AI总结：
提出GREAT框架，通过挖掘不变几何属性并进行类比推理，结合几何与视觉内容实现开放词汇3D物体可供性定位，并发布最大数据集PIADv2，实验验证其优越性。
摘要：
开放词汇3D物体可供性定位旨在根据任意指令预测3D物体上的“可操作区域”，这对于机器人泛化感知真实场景并响应操作变化至关重要。现有方法侧重于结合描绘交互的图像或语言与3D几何，以引入外部交互先验。然而，由于未能利用隐含的不变几何和潜在交互意图，它们仍易受限于有限的语义空间。通常，人类通过多步推理处理复杂任务，并借助联想和类比思维应对多样情境。鉴于此，我们提出GREAT（几何-意图协同推理）用于开放词汇3D物体可供性定位，这是一种新颖框架，挖掘物体不变几何属性，并在潜在交互场景中进行类比推理以形成可供性知识，将知识与几何和视觉内容充分结合以定位3D物体可供性。此外，我们引入了点图像可供性数据集v2（PIADv2），这是目前最大的3D物体可供性数据集，以支持该任务。大量实验证明了GREAT的有效性和优越性。代码和数据集可在https://yawen-shao.github.io/GREAT/获取。
英文摘要：
Open-Vocabulary 3D object affordance grounding aims to anticipate ``action possibilities'' regions on 3D objects with arbitrary instructions, which is crucial for robots to generically perceive real scenarios and respond to operational changes. Existing methods focus on combining images or languages that depict interactions with 3D geometries to introduce external interaction priors. However, they are still vulnerable to a limited semantic space by failing to leverage implied invariant geometries and potential interaction intentions. Normally, humans address complex tasks through multi-step reasoning and respond to diverse situations by leveraging associative and analogical thinking. In light of this, we propose GREAT (GeometRy-intEntion collAboraTive inference) for Open-Vocabulary 3D Object Affordance Grounding, a novel framework that mines the object invariant geometry attributes and performs analogically reason in potential interaction scenarios to form affordance knowledge, fully combining the knowledge with both geometries and visual contents to ground 3D object affordance. Besides, we introduce the Point Image Affordance Dataset v2 (PIADv2), the largest 3D object affordance dataset at present to support the task. Extensive experiments demonstrate the effectiveness and superiority of GREAT. The code and dataset are available at https://yawen-shao.github.io/GREAT/.
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。
