捕捉细粒度对齐提升3D功能检测
Capturing Fine-Grained Alignments Improves 3D Affordance Detection
AI总结：
本文提出LM-AD方法，通过功能查询模块利用预训练语言模型捕捉点云与文本的细粒度对齐，在3D AffordanceNet上提升功能检测的准确率和平均交并比。
摘要：
在这项工作中，我们解决了3D点云中功能检测的挑战，该任务需要有效捕捉点云与文本之间的细粒度对齐。现有方法通常难以建模这种对齐，导致在标准基准上的性能有限。这些方法的一个关键限制是它们依赖于点云和文本嵌入之间的简单余弦相似度，这缺乏细粒度推理所需的表达能力。为解决这一限制，我们提出了LM-AD，一种用于3D点云功能检测的新方法。此外，我们引入了功能查询模块（AQM），通过利用预训练语言模型高效捕捉点云与文本之间的细粒度对齐。我们证明了我们的方法在3D AffordanceNet数据集上的准确率和平均交并比方面优于现有方法。
英文摘要：
In this work, we address the challenge of affordance detection in 3D point clouds, a task that requires effectively capturing fine-grained alignments between point clouds and text. Existing methods often struggle to model such alignments, resulting in limited performance on standard benchmarks. A key limitation of these approaches is their reliance on simple cosine similarity between point cloud and text embeddings, which lacks the expressiveness needed for fine-grained reasoning. To address this limitation, we propose LM-AD, a novel method for affordance detection in 3D point clouds. Moreover, we introduce the Affordance Query Module (AQM), which efficiently captures fine-grained alignment between point clouds and text by leveraging a pretrained language model. We demonstrated that our method outperformed existing approaches in terms of accuracy and mean Intersection over Union on the 3D AffordanceNet dataset.
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。
