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 iscretizes 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 this https URL

介绍了一种基于神经辐射场的快速正交平面（Forplane）的新型高效重建变形组织的方法，将手术过程抽象为四维体积，通过正交神经平面对其进行分解，以减少内存使用和加快优化过程，通过引入空间时间重要采样方案和高效光线行进方法，在保持或提高质量的同时，大幅加速了优化和推理过程，为未来的手术应用提供了有价值的资源。