EndoSelf: Self-Supervised Monocular 3D Scene Reconstruction of Deformable Tissues with Neural Radiance Fields on Endoscopic Videos

Neural radiance field has recently emerged as a powerful representation to reconstruct deformable tissues from endoscopic videos. Previous methods mainly focus on depth-supervised approaches based on endoscopic datasets. As additional information, depth values were proven important in reconstructing deformable tissues by previous methods. However, collecting a large number of datasets with accurate depth values limits the applicability of these approaches for endoscopic scenes. To address this issue, we propose a novel self-supervised monocular 3D scene reconstruction method based on neural radiance fields without prior depth as supervision. We consider the monocular 3D reconstruction based on two approaches: ray-tracing-based neural radiance fields and structure-from-motion-based photogrammetry. We introduce structure from motion framework and leverage color values as a supervision to complete the self-supervised learning strategy. In addition, we predict the depth values from neural radiance fields and enforce the geometric constraint for depth values from adjacent views. Moreover, we propose a looped loss function to fully explore the temporal correlation between input images. The experimental results showed that the proposed method without prior depth outperformed the previous depth-supervised methods on two endoscopic datasets. Our code is available.