The accurate 3D reconstruction of deformable soft body tissues from endoscopic videos is a pivotal challenge in medical applications such as VR surgery and medical image analysis. Existing methods often struggle with accuracy and the ambiguity of hallucinated tissue parts, limiting their practical utility. In this work, we introduce EndoGaussians, a novel approach that employs Gaussian Splatting for dynamic endoscopic 3D reconstruction. This method marks the first use of Gaussian Splatting in this context, overcoming the limitations of previous NeRF-based techniques. Our method sets new state-of-the-art standards, as demonstrated by quantitative assessments on various endoscope datasets. These advancements make our method a promising tool for medical professionals, offering more reliable and efficient 3D reconstructions for practical applications in the medical field.

翻译：从内窥镜视频中对可变形软体组织进行精确的三维重建，是虚拟现实手术和医学图像分析等医疗应用中的关键挑战。现有方法常受限于精度不足以及组织部位幻觉导致的模糊性，从而制约了其实用价值。本研究提出EndoGaussians——一种采用高斯泼溅实现动态内窥镜三维重建的创新方法。该方法首次将高斯泼溅应用于此领域，突破了此前基于NeRF技术的局限性。通过对多种内窥镜数据集的定量评估，本方法树立了新的最优标准。这些进展使本方法成为医疗专业人员的有力工具，为医学领域的实际应用提供了更可靠、更高效的三维重建方案。