SG-3DGS: Sequential Growing 3D Gaussian Splatting for Scene Reconstruction of Monocular Endoscope Video

The reconstruction of monocular endoscope video scenes is essential for enhancing the application and analysis of surgical endoscopic images. However, restricted by the narrow space of endoscopic movement and the obstruction of vision within cavities, it is difficult for most conventional methods to perform high-quality reconstruction. To address these challenges, a novel dynamic growing 3D gaussian splatting architecture is proposed to construct the 3D model of endoscopic scene without precomputed camera poses or Structure from Motion. Firstly, to establish spatial feature associations between interframes, a 2D-3D displacement fields are designed by utilizing dense feature matches and depth prediction. On this basis, a novel displacement field variational optimization is developed to obtain relative poses by minimizing the energy functional associated with field transformation. Secondly, to address the constraint of the endoscopic view, by gaussian sequential transformation and differential gradient field optimization, a novel Sequential Gaussian Growing Module is proposed to grow the local gaussian model sequentially. Finally, a novel Forward-Reconstruction&Backward-Optimization architecture is proposed to generate the global gaussian model. The evaluation is conducted on two public endoscopic datasets: Scared and C3VD. The experimental results demonstrate that the proposed method outperforms state-of-the-art methods in both quantitative metrics (PSNR, SSIM, LPIPS, ATE, RMSE, MAE) and qualitative comparisons. The project page is https://iheckzza.github.io/DG-3DGS/.