3D Scene Change Modeling with Consistent Multi-View Aggregation

Change detection plays a vital role in scene monitoring, exploration, and continual reconstruction. Existing 3D change detection methods often exhibit spatial inconsistency in the detected changes and fail to explicitly separate pre- and post-change states. To address these limitations, we propose SCaR-3D, a novel 3D scene change detection framework that identifies object-level changes from a dense-view pre-change image sequence and sparse-view post-change images. Our approach consists of a signed-distance–based 2D differencing module followed by multi-view aggregation with voting and pruning; the aggragation strategy leverages the consistent nature of 3DGS and also robustly separates pre- and post-change states. Based on the detected change regions, we further develop a continual scene reconstruction strategy that selectively updates dynamic regions while preserving the unchanged areas. We also contribute CCS3D, a challenging synthetic dataset that allows flexible combinations of 3D change types to support controlled evaluations. Extensive experiments demonstrate that our method achieves both high accuracy and efficiency, outperforming existing methods.

We first employ COLMAP for image registration, producing paired pre-change renders and post-change captures. In the 2D Difference Generation stage, features are extracted and a signed distance metric is applied to separate the change regions into two sets. After that, the 3D Difference Aggregation stage integrates multi-view differences through voting, pruning, and segmentation validation. Finally, the change masks are applied to the reconstruction process to selectively update the 3D scene.