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arXiv 2602.02148eess.SP
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利用可重构智能表面的无线无遮挡感知用于单视角3D重建

RIS-Aided Wireless Amodal Sensing for Single-View 3D Reconstruction

Peking University Shenzhen Graduate School(北京大学深圳研究生院);Peking University(北京大学);Hunan Institute of Advanced Sensing and Information Technology, Xiangtan University(湘潭大学先进传感与信息技术湖南研究院)

Yuhan Wang, Haobo Zhang, Qingyu Liu, Hongliang Zhang, Lingyang Song

AI总结:

本文提出利用可重构智能表面提升无线无遮挡感知的3D重建方法,通过优化RIS相移减少重建误差,实验显示误差降低达56.73%。

摘要:

无遮挡感知对于各种现实世界感知应用至关重要,因为它可以在复杂环境中恢复部分被遮挡物体的完整形状。在各种无遮挡感知范式中,无线无遮挡感知是一种潜在的解决方案,因为它具有环境鲁棒性、隐私保护和低成本的优势。然而,由于空间分辨率低,无线系统获得的传感数据对于形状重建来说是稀疏的,这个问题在复杂环境中存在遮挡时进一步加剧。为了解决这个问题,我们提出了一种利用大规模可重构智能表面(RIS)的无线无遮挡感知方案,利用大规模RIS来提高空间分辨率,并创建可以绕过障碍物的反射路径。此外,还采用生成学习模型,基于从RIS视角捕获的传感数据来重建完整形状。在这样的系统中,优化RIS相移具有挑战性,因为RIS相移与无遮挡感知精度之间的关系复杂且没有闭合表达式。为了解决这一挑战,我们开发了一个误差预测模型,该模型学习从RIS相移到无遮挡感知精度的映射,并基于此映射优化RIS相移。在基准数据集上的实验结果表明,与相同数量RIS配置的传统方案相比,我们的方法在重建误差上至少减少了56.73%。

英文摘要:

Amodal sensing is critical for various real-world sensing applications because it can recover the complete shapes of partially occluded objects in complex environments. Among various amodal sensing paradigms, wireless amodal sensing is a potential solution due to its advantages of environmental robustness, privacy preservation, and low cost. However, the sensing data obtained by wireless system is sparse for shape reconstruction because of the low spatial resolution, and this issue is further intensified in complex environments with occlusion. To address this issue, we propose a Reconfigurable Intelligent Surface (RIS)-aided wireless amodal sensing scheme that leverages a large-scale RIS to enhance the spatial resolution and create reflection paths that can bypass the obstacles. A generative learning model is also employed to reconstruct the complete shape based on the sensing data captured from the viewpoint of the RIS. In such a system, it is challenging to optimize the RIS phase shifts because the relationship between RIS phase shifts and amodal sensing accuracy is complex and the closed-form expression is unknown. To tackle this challenge, we develop an error prediction model that learns the mapping from RIS phase shifts to amodal sensing accuracy, and optimizes RIS phase shifts based on this mapping. Experimental results on the benchmark dataset show that our method achieves at least a 56.73% reduction in reconstruction error compared to conventional schemes under the same number of RIS configurations.

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。

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