AffordMatcher: 从视觉符号中学习3D场景中的 affordance
AffordMatcher: Affordance Learning in 3D Scenes from Visual Signifiers
AI总结：
本文提出AffordMatcher，通过结合点云和图像实例，利用视觉符号实现更精确的affordance区域识别，基于大规模数据集验证了方法有效性。
摘要：
affordance学习在许多应用中是一个复杂的挑战，现有方法主要关注物体的几何结构、视觉知识和affordance标签来确定可交互区域。然而，将这种学习能力扩展到场景层面显著更加复杂，因为整合物体和场景级别的语义并不直观。在本文中，我们引入了AffordBridge，这是一个包含291,637个功能交互注释的大型数据集，覆盖685个高分辨率室内场景，形式为点云。我们的affordance注释通过与场景内相同实例关联的RGB图像进行补充。基于此数据集，我们提出AffordMatcher，一种affordance学习方法，建立了基于图像和点云实例之间的连贯语义对应关系，用于关键点匹配，从而基于视觉符号实现更精确的affordance区域识别。在我们的数据集上的实验结果表明，与其它方法相比，我们的方法效果更佳。
英文摘要：
Affordance learning is a complex challenge in many applications, where existing approaches primarily focus on the geometric structures, visual knowledge, and affordance labels of objects to determine interactable regions. However, extending this learning capability to a scene is significantly more complicated, as incorporating object- and scene-level semantics is not straightforward. In this work, we introduce AffordBridge, a large-scale dataset with 291,637 functional interaction annotations across 685 high-resolution indoor scenes in the form of point clouds. Our affordance annotations are complemented by RGB images that are linked to the same instances within the scenes. Building upon our dataset, we propose AffordMatcher, an affordance learning method that establishes coherent semantic correspondences between image-based and point cloud-based instances for keypoint matching, enabling a more precise identification of affordance regions based on cues, so-called visual signifiers. Experimental results on our dataset demonstrate the effectiveness of our approach compared to other methods.
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。
