bibtext: @InProceedings{ Zhe_Misaligned_MICCAI2024,
                   author = { Zheng, Jieyu and Li, Xiaojian and Mo, Hangjie and Li, Ling and Ma, Xiang },
                   title = { { Misaligned 3D Texture Optimization in MIS Utilizing Generative Framework } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15006 },
                   month = {October},
                   pages = { pending },
                }
abstract: Three-dimensional reconstruction of the surgical area based on intraoperative laparoscopic videos can restore 2D information to 3D space, providing a solid technical foundation for many applications in computer-assisted surgery. SLAM methods often suffer from imperfect pose estimation and tissue motion, leading to the loss of original texture information. On the other hand, methods like Neural Radiance Fields and 3D Gaussian Split require offline processing and lack generalization capabilities. To overcome these limitations, we explore a texture optimization method that generates high resolution and continuous texture. It designs a mechanism for transforming 3D point clouds into 2D texture space and utilizes a generative network architecture to design 2D registration and image fusion modules. Experimental results and comparisons with state-of-the-art techniques demonstrate the effectiveness of this method in preserving the high-fidelity texture.
title: Misaligned 3D Texture Optimization in MIS Utilizing Generative Framework
authors: Zheng, Jieyu Li, Xiaojian Mo, Hangjie Li, Ling Ma, Xiang