检测3D场景移除物体后残留语义信息，保障隐私安全
Is there anything left? Measuring semantic residuals of objects removed from 3D Gaussian Splatting
- 提出量化评估移除物体后是否残留可推理的语义信息
- 实验证明主流3D重建方法移除后仍存可识别残留物
- 适用于需隐私保护的3D地图共享场景
随着可训练场景表示技术的发展，用户可通过语义概念直观搜索与编辑3D场景。现有评估多关注搜索或提取的准确性，本文则聚焦逆问题：移除物体后还剩多少原始信息？该问题在用户重建3D地图并欲移除私人物体前共享时尤为重要。本文首次系统研究此问题，提出一种定量评估方法，用于判断移除操作是否留下可被推理的语义残留。若存在此类残留，则场景不具隐私性。实验表明，所提指标在主流场景表示上均具意义，且与用户研究结果一致。此外，本文还提出基于空间与语义一致性的优化移除方法。
原文摘要 · Abstract (English)
Searching in and editing 3D scenes has become extremely intuitive with trainable scene representations that allow linking human concepts to elements in the scene. These operations are often evaluated on the basis of how accurately the searched element is segmented or extracted from the scene. In this paper, we address the inverse problem, that is, how much of the searched element remains in the scene after it is removed. This question is particularly important in the context of privacy-preserving mapping when a user reconstructs a 3D scene and wants to remove private elements before sharing the map. To the best of our knowledge, this is the first work to address this question. To answer this, we propose a quantitative evaluation that measures whether a removal operation leaves object residuals that can be reasoned over. The scene is not private when such residuals are present. Experiments on state-of-the-art scene representations show that the proposed metrics are meaningful and consistent with the user study that we also present. We also propose a method to refine the removal based on spatial and semantic consistency.
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