Enhanced method for digital projection of root carving paper cultural relics: Reconstruction and rendering optimization based on 3D Gaussian splatting

This study proposes a 3D reconstruction method based on 3D Gaussian Splatting (3DGS) for cultural relics, such as root carvings and paper-based relics, which are characterized by fine fiber structures and complex surface details. A consumer-grade smartphone was used to capture multi-view images of the root carvings and paper-based relics. Subsequently, the FFmpeg tool was employed to extract image frames, which serve as input images. COLMAP software was utilized to perform feature matching, Structure from Motion (SfM) computation, and camera pose estimation. This process generated a sparse point cloud, which was used as the initialization data for the 3D Gaussian distribution. This workflow produced high-quality reconstruction of the micro-details of paper materials (such as fiber textures and carved indentations). Comparative evaluations were conducted against Neural Radiance Fields (NeRF) and Instant Neural Graphics Primitives (Instant-NGP). The proposed 3DGS framework achieved superior performance. Compared with NeRF, the average PSNR increased by 39.60%, SSIM by 65.84%, and LPIPS by 90.57%, while the reconstruction time was shortened by 68.89%. Compared with Instant-NGP, the framework achieved an increase of 31.56% in average PSNR, 42.58% in SSIM, a decrease of 89.21% in LPIPS, and a reduction of 48.15% in reconstruction time.