Intraoperative imaging techniques for reconstructing deformable tissues in vivo are pivotal for advanced surgical systems. Existing methods either compromise on rendering quality or are excessively computationally intensive, often demanding dozens of hours to perform, which significantly hinders their practical application. In this paper, we introduce Fast Orthogonal Plane (Forplane), a novel, efficient framework based on neural radiance fields (NeRF) for the reconstruction of deformable tissues. We conceptualize surgical procedures as 4D volumes, and break them down into static and dynamic fields comprised of orthogonal neural planes. This factorization discretizes the four-dimensional space, leading to a decreased memory usage and faster optimization. A spatiotemporal importance sampling scheme is introduced to improve performance in regions with tool occlusion as well as large motions and accelerate training. An efficient ray marching method is applied to skip sampling among empty regions, significantly improving inference speed. Forplane accommodates both binocular and monocular endoscopy videos, demonstrating its extensive applicability and flexibility. Our experiments, carried out on two in vivo datasets, the EndoNeRF and Hamlyn datasets, demonstrate the effectiveness of our framework. In all cases, Forplane substantially accelerates both the optimization process (by over 100 times) and the inference process (by over 15 times) while maintaining or even improving the quality across a variety of non-rigid deformations. This significant performance improvement promises to be a valuable asset for future intraoperative surgical applications. The code of our project is now available at https://github.com/Loping151/ForPlane .

中文翻译：

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通过正交神经平面进行高效的可变形组织重建


用于在体内重建可变形组织的术中成像技术对于先进的手术系统至关重要。现有方法要么在渲染质量上妥协，要么计算量过大，通常需要数十小时才能完成，这极大地阻碍了它们的实际应用。在本文中，我们介绍了快速正交平面 （Forplane），这是一种基于神经辐射场 （NeRF） 的新型高效框架，用于重建可变形组织。我们将外科手术概念化为 4D 体积，并将它们分解为由正交神经平面组成的静态和动态场。这种因式分解使四维空间离散化，从而减少内存使用并加快优化速度。引入时空重要性采样方案，以提高工具遮挡和大运动区域的性能并加速训练。采用一种高效的光线行进方法在空白区域之间跳过采样，显著提高了推理速度。Forplane 可容纳双眼和单眼内窥镜视频，展示了其广泛的适用性和灵活性。我们在两个体内数据集 EndoNeRF 和 Hamlyn 数据集上进行的实验证明了我们框架的有效性。在所有情况下，Forplane 都大大加快了优化过程（超过 100 倍）和推理过程（超过 15 倍），同时保持甚至提高了各种非刚性变形的质量。这种显著的性能改进有望成为未来术中手术应用的宝贵资产。我们项目的代码现在可以在 https://github.com/Loping151/ForPlane 上获得。