构形：基于生成重建的3D构形定位
Affostruction: 3D Affordance Grounding with Generative Reconstruction
AI总结：
本文提出Affostruction框架，通过生成式重建完整物体几何并基于全形体进行构形定位，优于现有方法，在构形定位和3D重建上取得显著成绩。
摘要：
本文针对RGBD图像中物体的构形定位问题，旨在定位与文本查询描述的物体动作对应的表面区域。现有方法仅在可见表面预测构形区域，我们提出Affostruction生成框架，从部分RGBD观测中重建完整物体几何并基于全形体进行构形定位。我们的方法引入稀疏体素融合多视角特征以实现常数复杂度生成式重建，一种基于流的公式捕捉构形分布的固有模糊性，以及由预测构形引导的主动视角选择策略。Affostruction在具有挑战性的基准上显著优于现有方法，实现19.1 aIoU的构形定位和32.67 IoU的3D重建。
英文摘要：
This paper addresses the problem of affordance grounding from RGBD images of an object, which aims to localize surface regions corresponding to a text query that describes an action on the object. While existing methods predict affordance regions only on visible surfaces, we propose Affostruction, a generative framework that reconstructs complete object geometry from partial RGBD observations and grounds affordances on the full shape including unobserved regions. Our approach introduces sparse voxel fusion of multi-view features for constant-complexity generative reconstruction, a flow-based formulation that captures the inherent ambiguity of affordance distributions, and an active view selection strategy guided by predicted affordances. Affostruction outperforms existing methods by large margins on challenging benchmarks, achieving 19.1 aIoU on affordance grounding and 32.67 IoU for 3D reconstruction.
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。
