OpticFusion：融合白光干涉测量与光学显微镜的多模态神经隐式微结构三维重建
OpticFusion: Multi-Modal Neural Implicit 3D Reconstruction of Microstructures by Fusing White Light Interferometry and Optical Microscopy
AI总结：
针对传统白光干涉测量无法获取微结构表面自然色彩的问题，提出OpticFusion方法，融合多视角白光干涉与光学显微镜图像，通过两步数据关联、神经隐式表示与色彩分解实现带纹理的精细三维重建，为微尺度研究提供低成本有效工具。
摘要：
白光干涉测量法（White Light Interferometry, WLI）是一种用于测量微结构三维形貌的精密光学工具。然而，传统WLI无法捕获样品表面的自然色彩，而这对许多同时需要三维几何结构与色彩信息的微尺度研究应用至关重要。此前的方法曾尝试通过改造WLI硬件与分析软件来克服这一局限，但这类方案通常成本高昂。\n在本研究中，我们首次从计算机视觉多模态重建的视角应对这一挑战。我们提出了OpticFusion这一全新方法，该方法借助额外的数字光学显微镜（optical microscope, OM），利用多视角WLI与OM图像实现带有自然色彩纹理的三维重建。我们的方法采用两步数据关联流程来获取WLI与OM数据的位姿。通过利用神经隐式表示，我们融合多模态数据，并应用色彩分解技术提取样品的自然色彩。\n在我们构建的包含多种微尺度样品的多模态数据集上测试表明，OpticFusion可实现带有色彩纹理的精细三维重建。我们的方法为众多微尺度研究领域的实际应用提供了有效工具。源代码与真实世界数据集可在https://github.com/zju3dv/OpticFusion获取。
英文摘要：
White Light Interferometry (WLI) is a precise optical tool for measuring the 3D topography of microstructures. However, conventional WLI cannot capture the natural color of a sample's surface, which is essential for many microscale research applications that require both 3D geometry and color information. Previous methods have attempted to overcome this limitation by modifying WLI hardware and analysis software, but these solutions are often costly. In this work, we address this challenge from a computer vision multi-modal reconstruction perspective for the first time. We introduce OpticFusion, a novel approach that uses an additional digital optical microscope (OM) to achieve 3D reconstruction with natural color textures using multi-view WLI and OM images. Our method employs a two-step data association process to obtain the poses of WLI and OM data. By leveraging the neural implicit representation, we fuse multi-modal data and apply color decomposition technology to extract the sample's natural color. Tested on our multi-modal dataset of various microscale samples, OpticFusion achieves detailed 3D reconstructions with color textures. Our method provides an effective tool for practical applications across numerous microscale research fields. The source code and our real-world dataset are available at https://github.com/zju3dv/OpticFusion.
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。
