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, leveraging the consistent nature of 3DGS to robustly separate pre- and post-change states. 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.

翻译：变化检测在场景监控、探索与持续重建中发挥着关键作用。现有的三维变化检测方法常存在检测结果的空间不一致性，且未能明确分离变化前后的场景状态。为克服这些局限，本文提出SCaR-3D——一种新颖的三维场景变化检测框架，可从密集视角的变化前图像序列与稀疏视角的变化后图像中识别物体级变化。该方法包含基于符号距离的二维差分模块，以及通过投票与剪枝实现的多视角聚合模块，利用3DGS的几何一致性特性鲁棒地分离变化前后状态。我们进一步提出持续场景重建策略，在保留未变化区域的同时选择性更新动态区域。此外，我们构建了CCS3D合成数据集，该数据集支持三维变化类型的灵活组合，可用于受控评估实验。大量实验表明，本方法在精度与效率上均优于现有方法。