单目神经触觉场重建
Monocular Reconstruction of Neural Tactile Fields
AI总结：
本文提出了一种基于单目RGB图像的神经触觉场重建方法，通过交互感知的3D表示提升机器人路径规划能力，实验显示其在3D重建任务中表现优于现有方法。
摘要：
在真实世界中运行的机器人必须通过变形、变形和重新配置的环境进行规划，这需要能够超越静态几何占据的交互感知3D表示。为此，我们引入了神经触觉场，这是一种新的3D表示方法，将空间位置映射到接触时的预期触觉响应。我们的模型从单目RGB图像预测这些神经触觉场——这是首次实现此类方法。当与现成的路径规划器结合时，神经触觉场使机器人能够生成避开高阻力物体的同时故意通过低阻力区域（例如植被）的路径，而不是将所有占据空间视为等同不可通过。经验上，我们的学习框架在体积3D重建方面比最先进的单目3D重建方法（LRM和Direct3D）提高了85.8%，在表面重建方面提高了26.7%。
英文摘要：
Robots operating in the real world must plan through environments that deform, yield, and reconfigure under contact, requiring interaction-aware 3D representations that extend beyond static geometric occupancy. To address this, we introduce neural tactile fields, a novel 3D representation that maps spatial locations to the expected tactile response upon contact. Our model predicts these neural tactile fields from a single monocular RGB image -- the first method to do so. When integrated with off-the-shelf path planners, neural tactile fields enable robots to generate paths that avoid high-resistance objects while deliberately routing through low-resistance regions (e.g. foliage), rather than treating all occupied space as equally impassable. Empirically, our learning framework improves volumetric 3D reconstruction by $85.8\%$ and surface reconstruction by $26.7\%$ compared to state-of-the-art monocular 3D reconstruction methods (LRM and Direct3D).
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。
