GelSplitter：基于近红外和可见光图像的触觉重建
GelSplitter: Tactile Reconstruction from Near Infrared and Visible Images
AI总结：
本文提出GelSplitter多模态视觉触觉传感器框架，通过集成近红外与可见光相机并设计光度融合立体神经网络，实现了高精度的3D触觉几何重建。
摘要：
类GelSight视觉触觉（VT）传感器作为机器人的高分辨率触觉传感技术日益普及，能够使用单台RGB相机测量接触几何形状。然而，VT传感器的多模态感知发展仍面临挑战，受限于单相机限制。本文提出GelSplitter，一种采用同步多模态相机、类似人类触觉感受器的新型多模态VT传感器框架。我们聚焦3D触觉重建，实现了紧凑的传感器结构，即使增加棱镜和近红外（NIR）相机，仍保持与现有VT传感器相当的尺寸。我们还设计了光度融合立体神经网络（PFSNN），从红外和可见光图像中估计物体表面法线并重建触觉几何。结果表明，RGB与NIR融合的精度高于仅使用RGB图像。此外，GelSplitter框架支持灵活配置不同相机传感器组合，如RGB与热成像。
英文摘要：
The GelSight-like visual tactile (VT) sensor has gained popularity as a high-resolution tactile sensing technology for robots, capable of measuring touch geometry using a single RGB camera. However, the development of multi-modal perception for VT sensors remains a challenge, limited by the mono camera. In this paper, we propose the GelSplitter, a new framework approach the multi-modal VT sensor with synchronized multi-modal cameras and resemble a more human-like tactile receptor. Furthermore, we focus on 3D tactile reconstruction and implement a compact sensor structure that maintains a comparable size to state-of-the-art VT sensors, even with the addition of a prism and a near infrared (NIR) camera. We also design a photometric fusion stereo neural network (PFSNN), which estimates surface normals of objects and reconstructs touch geometry from both infrared and visible images. Our results demonstrate that the accuracy of RGB and NIR fusion is higher than that of RGB images alone. Additionally, our GelSplitter framework allows for a flexible configuration of different camera sensor combinations, such as RGB and thermal imaging.
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。
