We present 3DGS-CD, the first 3D Gaussian Splatting (3DGS)-based method for detecting physical object rearrangements in 3D scenes. Our approach estimates 3D object-level changes by comparing two sets of unaligned images taken at different times. Leveraging 3DGS's novel view rendering and EfficientSAM's zero-shot segmentation capabilities, we detect 2D object-level changes, which are then associated and fused across views to estimate 3D changes. Our method can detect changes in cluttered environments using sparse post-change images within as little as 18s, using as few as a single new image. It does not rely on depth input, user instructions, object classes, or object models -- An object is recognized simply if it has been re-arranged. Our approach is evaluated on both public and self-collected real-world datasets, achieving up to 14% higher accuracy and three orders of magnitude faster performance compared to the state-of-the-art radiance-field-based change detection method. This significant performance boost enables a broad range of downstream applications, where we highlight three key use cases: object reconstruction, robot workspace reset, and 3DGS model update.
我们提出了3DGS-CD，这是首个基于3D Gaussian Splatting (3DGS) 的方法，用于检测3D场景中物体的物理重排。该方法通过比较在不同时间拍摄的两组未对齐图像，估计3D物体级别的变化。利用3DGS的新颖视图渲染和EfficientSAM的零样本分割能力，我们检测2D物体级别的变化，并在不同视图间关联和融合，最终估计出3D变化。我们的方法能够在杂乱环境中通过稀疏的变化后图像检测重排，检测时间仅需18秒，甚至只需一张新图像。该方法不依赖深度输入、用户指令、物体类别或物体模型——物体是否被重排仅通过位置变化来识别。我们在公共和自采集的真实世界数据集上进行了评估，与最先进的基于辐射场的变化检测方法相比，精度提高了多达14%，性能提升了三个数量级。这种显著的性能提升使得广泛的下游应用成为可能，其中我们重点介绍了三个关键用例：物体重建、机器人工作空间复位以及3DGS模型更新。