An object can disappear from a 3D scene, yet still be detectable. Even after visual removal, modern vision models may infer what was originally present. In this work, we introduce a novel benchmark and evaluation framework to quantify semantic residuals, the unintended cues left behind after object removal in 3D Gaussian Splatting. We conduct experiments across a diverse set of indoor and outdoor scenes, showing that current methods often preserve semantic information despite the absence of visual geometry. Notably, even when removal is followed by inpainting, residual cues frequently remain detectable by foundation models. We also present Remove360, a real-world dataset of pre- and post-removal RGB captures with object-level masks. Unlike prior datasets focused on isolated object instances, Remove360 contains complex, cluttered scenes that enable evaluation of object removal in full-scene settings. By leveraging the ground-truth post-removal images, we directly assess whether semantic presence is eliminated and whether downstream models can still infer what was removed. Our results reveal a consistent gap between geometric removal and semantic erasure, exposing critical limitations in existing 3D editing pipelines and highlighting the need for privacy-aware removal methods that eliminate recoverable cues, not only visible geometry. Dataset and evaluation code are publicly available.

翻译：物体可能从三维场景中消失，却仍可被检测到。即使经过视觉移除，现代视觉模型仍可推断出最初存在的内容。在本工作中，我们引入了一种新颖的基准测试与评估框架，用于量化三维高斯泼溅中物体移除后留下的非预期的语义残留线索。我们在多样化的室内外场景中进行了实验，表明当前方法即使缺少视觉几何信息，也往往保留了语义信息。值得注意的是，即使在移除后进行修复，残留线索仍常能被基础模型检测到。我们还展示了Remove360，这是一个包含物体级别掩码的移除前后RGB图像的真实场景数据集。与先前专注于孤立物体实例的数据集不同，Remove360包含复杂、杂乱的场景，能够评估在全场景设置下的物体移除效果。通过利用移除后的真实图像，我们直接评估语义存在是否被消除，以及下游模型是否仍能推断出被移除的内容。我们的结果揭示了几何移除与语义擦除之间一直存在的差距，暴露了现有三维编辑管线的关键局限，并凸显了对能消除可恢复线索（而不仅是可见几何信息）的隐私保护移除方法的需求。数据集与评估代码已公开。