Render-to-Real Image-Based Change Detection of Outdoor Infrastructure Using 3D Gaussian Splatting

Abstract. This study proposes a framework for detecting changes in outdoor civil infrastructure using bi-temporal images and validates its effectiveness through experiments on real-world datasets. The proposed method performs change detection by comparing a 3D Gaussian Splatting (3DGS) model reconstructed from multi-view images acquired before changes occur with a single real image captured from a new observation viewpoint after changes. The processing pipeline consists of: (1) construction of the 3DGS model, (2) generation of an initial rendered image corresponding to the post-change real image, (3) feature matching between the rendered image and the real image followed by camera pose estimation, and (4) change detection. Experiments conducted on a sediment control dam and a bridge dataset demonstrate that the proposed method achieves a maximum Intersection over Union (IoU) of 0.82 for change detection. Furthermore, compared to a baseline method based on bi-temporal real image pairs, the proposed method improves IoU by up to 24 percentage points. The results also indicate that even under limited acquisition conditions after changes, accurate change detection can be achieved when the 3DGS reconstruction quality and pose estimation are sufficiently reliable.