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English(EN) BridgeAfford: Bridging Query-Target Gap for Generalizable 3D Affordance Grounding

BridgeAfford框架增强3D可供性识别的泛化能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了BridgeAfford，一个旨在提高语言引导的3D可供性识别泛化能力的新框架。该方法解决了查询特征和目标区域特征之间的语义鸿沟，而这会阻碍在未见过物体上的性能。BridgeAfford采用交互区域对齐（Interaction Region Alignment）来对齐不同物体上相似的交互区域，并采用视觉证据聚合（Visual Evidence Aggregation）来精炼注意力并引导查询到正确的区域。在LASO和PIAD数据集上的实验表明，BridgeAfford的性能显著优于现有方法，尤其是在新颖物体上。 AI

影响 提高了AI系统根据自然语言指令理解和与3D物体交互的能力。

排序理由 这是一篇研究论文，详细介绍了一个特定AI任务的新框架和方法论。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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BridgeAfford框架增强3D可供性识别的泛化能力

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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yijie Su, Yakun Zhu, Yi Bin, Pengpeng Zeng, Liang Chen, Guoqing Wang, Jingkuan Song, Heng Tao Shen ·

    BridgeAfford：弥合查询-目标差距，实现可泛化的3D可供性识别

    arXiv:2610.21918v1 Announce Type: cross Abstract: Language-guided 3D affordance grounding aims to localize functional regions on 3D objects according to interaction intents. However, existing methods exhibit poor generalization to unseen objects. One challenge is the query-target…

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