变形组织四维重建的基础模型引导高斯溅射

Yifan Liu;Chenxin Li;Hengyu Liu;Chen Yang;Yixuan Yuan
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摘要

从内窥镜视频中重建可变形的解剖结构是一个关键且有前途的研究课题,可以实现先进的外科应用并改善患者的预后。虽然现有的手术场景重建方法已经取得了显著的进步,但由于使用神经辐射场,它们的渲染速度往往很慢,限制了它们在现实世界应用中的实际可行性。为了克服这一瓶颈,我们提出了EndoGaussian,这是一个集成了3D高斯飞溅表示优势的框架,允许高保真组织重建,高效训练和实时渲染。具体来说,我们提供了一个基础模型驱动初始化(FMI)模块,该模块从多个视觉基础模型(vfm)中提取3D线索,以快速构建高斯初始化的初步场景结构。然后,设计了一种时空高斯跟踪(SGT)方法,利用具有时空先验的多尺度HexPlane对场景动态进行高效建模。此外,为了提高大变形场景的动力学建模能力,EndoGaussian集成了运动感知帧合成(Motion-aware Frame Synthesis, MFS),自适应合成新帧作为额外的训练约束。在公共数据集上的实验结果表明,EndoGaussian的有效性优于先前的最先进的方法,包括更高的渲染速度(168 FPS,实时),增强的渲染质量(38.555 PSNR),以及减少的训练开销(2分钟/场景)。这些结果强调了EndoGaussian在显著推进术中手术应用方面的潜力,为临床场景中更准确、高效的实时手术指导和决策铺平了道路。代码可从https://github.com/CUHK-AIM-Group/EndoGaussian获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Foundation Model-Guided Gaussian Splatting for 4D Reconstruction of Deformable Tissues
Reconstructing deformable anatomical structures from endoscopic videos is a pivotal and promising research topic that can enable advanced surgical applications and improve patient outcomes. While existing surgical scene reconstruction methods have made notable progress, they often suffer from slow rendering speeds due to using neural radiance fields, limiting their practical viability in real-world applications. To overcome this bottleneck, we propose EndoGaussian, a framework that integrates the strengths of 3D Gaussian Splatting representations, allowing for high-fidelity tissue reconstruction, efficient training, and real-time rendering. Specifically, we dedicate a Foundation Model-driven Initialization (FMI) module, which distills 3D cues from multiple vision foundation models (VFMs) to swiftly construct the preliminary scene structure for Gaussian initialization. Then, a Spatio-temporal Gaussian Tracking (SGT) is designed, efficiently modeling scene dynamics using the multi-scale HexPlane with spatio-temporal priors. Furthermore, to improve the dynamics modeling ability for scenes with large deformation, EndoGaussian integrates Motion-aware Frame Synthesis (MFS) to adaptively synthesize new frames as extra training constraints. Experimental results on public datasets demonstrate EndoGaussian’s efficacy against prior state-of-the-art methods, including superior rendering speed (168 FPS, real-time), enhanced rendering quality (38.555 PSNR), and reduced training overhead (within 2 min/scene). These results underscore EndoGaussian’s potential to significantly advance intraoperative surgery applications, paving the way for more accurate and efficient real-time surgical guidance and decision-making in clinical scenarios. Code is available at: https://github.com/CUHK-AIM-Group/EndoGaussian.
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