Abstract High-fidelity 3D documentation of outdoor cultural heritage is essential for conservation and digital preservation, yet remains challenging under uncontrolled conditions with monocular inputs. This paper presents a monocular Gaussian Splatting SLAM with viewpoint-aware optimization framework tailored for large-scale heritage digitization. The method integrates pointmap-based pose initialization with a viewpoint-aware optimization strategy to mitigate scale drift and maintain geometric consistency under wide-baseline observations and complex illumination. Leveraging the explicit representation and efficient optimization of 3D Gaussian splatting, the framework achieves robust performance in large outdoor environments. Experiments on real-world heritage datasets and public benchmarks (Tanks and Temples) demonstrate improved trajectory accuracy and rendering quality over state-of-the-art GS-SLAM methods. Beyond performance gains, the approach supports structurally coherent and interpretable digital representations, contributing to more reliable documentation and understanding of cultural heritage.
Fan et al. (Wed,) studied this question.