IAAO：面向3D环境中关节物体的交互式可供性学习
IAAO: Interactive Affordance Learning for Articulated Objects in 3D Environments
AI总结：
本文提出IAAO框架，利用大型基础模型和3D高斯泼溅分三阶段估计关节物体的交互可供性与关节参数，实现稳健的物体交互操控。
摘要：
本文提出了IAAO，一种新颖的框架，为智能体构建显式的3D模型，使其通过交互理解环境中的关节物体。与以往依赖任务特定网络和对可动部件假设的方法不同，我们的IAAO利用大型基础模型分三个阶段估计交互可供性和部件关节。首先，我们使用3D高斯泼溅（3DGS），通过从多视图图像中蒸馏掩码特征和视图一致的标签，为每个物体状态构建层次化特征和标签场。然后，我们对3D高斯原语执行物体级和部件级查询，以识别静态和关节元素，估计全局变换和局部关节参数以及可供性。最后，基于估计的变换对不同状态的场景进行合并和细化，从而实现稳健的基于可供性的物体交互和操控。实验结果证明了我们方法的有效性。
英文摘要：
This work presents IAAO, a novel framework that builds an explicit 3D model for intelligent agents to gain understanding of articulated objects in their environment through interaction. Unlike prior methods that rely on task-specific networks and assumptions about movable parts, our IAAO leverages large foundation models to estimate interactive affordances and part articulations in three stages. We first build hierarchical features and label fields for each object state using 3D Gaussian Splatting (3DGS) by distilling mask features and view-consistent labels from multi-view images. We then perform object- and part-level queries on the 3D Gaussian primitives to identify static and articulated elements, estimating global transformations and local articulation parameters along with affordances. Finally, scenes from different states are merged and refined based on the estimated transformations, enabling robust affordance-based interaction and manipulation of objects. Experimental results demonstrate the effectiveness of our method.
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。
