代理变换：通过代理注意力预塑形点云流形用于三维视觉定位
ProxyTransformation: Preshaping Point Cloud Manifold With Proxy Attention For 3D Visual Grounding
AI总结：
针对自我中心三维视觉定位中点云冗余噪声干扰目标流形的问题，提出代理变换方法，利用可变形点聚类和代理注意力引导子流形变换，在简单和困难目标上分别提升7.49%和4.60%，并降低40.6%计算开销，达到新SOTA。
摘要：
具身智能要求智能体基于语言指令实时与三维环境交互。该领域的一项基础任务是自我中心三维视觉定位。然而，从RGB-D图像渲染出的点云保留了大量冗余背景数据和固有噪声，这两者都可能干扰目标区域的流形结构。现有的点云增强方法通常需要繁琐的过程来改善流形，不适合实时任务。我们提出适用于多模态任务的代理变换，以高效改善点云流形。我们的方法首先利用可变形点聚类来识别目标区域中的点云子流形。然后，我们提出一个代理注意力模块，利用多模态代理来引导点云变换。基于代理注意力，我们设计了一个子流形变换生成模块，其中文本信息全局引导不同子流形的平移向量，优化目标区域的相对空间关系。同时，图像信息引导每个子流形内的线性变换，细化目标区域的局部点云流形。大量实验表明，代理变换显著优于所有现有方法，在简单目标上实现了7.49%的显著提升，在困难目标上提升了4.60%，同时将注意力块的计算开销减少了40.6%。这些结果在自我中心三维视觉定位中确立了新的最先进水平，展示了我们方法的有效性和鲁棒性。
英文摘要：
Embodied intelligence requires agents to interact with 3D environments in real time based on language instructions. A foundational task in this domain is ego-centric 3D visual grounding. However, the point clouds rendered from RGB-D images retain a large amount of redundant background data and inherent noise, both of which can interfere with the manifold structure of the target regions. Existing point cloud enhancement methods often require a tedious process to improve the manifold, which is not suitable for real-time tasks. We propose Proxy Transformation suitable for multimodal task to efficiently improve the point cloud manifold. Our method first leverages Deformable Point Clustering to identify the point cloud sub-manifolds in target regions. Then, we propose a Proxy Attention module that utilizes multimodal proxies to guide point cloud transformation. Built upon Proxy Attention, we design a submanifold transformation generation module where textual information globally guides translation vectors for different submanifolds, optimizing relative spatial relationships of target regions. Simultaneously, image information guides linear transformations within each submanifold, refining the local point cloud manifold of target regions. Extensive experiments demonstrate that Proxy Transformation significantly outperforms all existing methods, achieving an impressive improvement of 7.49% on easy targets and 4.60% on hard targets, while reducing the computational overhead of attention blocks by 40.6%. These results establish a new SOTA in ego-centric 3D visual grounding, showcasing the effectiveness and robustness of our approach.
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。
