用微小扰动块破坏多视角3D重建，保护隐私不被滥用。
PatchPoison: Poisoning Multi-View Datasets to Degrade 3D Reconstruction
- 在每张图边缘加高频棋盘块，干扰特征匹配
- 使3DGS重建误差提升6.8倍，人眼难察觉
- 无需修改流程，内容创作者可直接使用
3D高斯点阵（3DGS）最近实现了从随意拍摄的多视角图像中生成高度逼真的3D重建。然而，这种易用性引发了隐私担忧：公开的图像或视频可能被用于未经授权地重建场景或物体的详细3D模型。我们提出PatchPoison，一种轻量级的数据集投毒方法，以阻止未经授权的3D重建。与全局扰动不同，PatchPoison在多视角数据集中的每张图像边缘注入一个小型高频对抗性补丁（结构化棋盘），该补丁旨在通过引入虚假对应关系，系统性地扭曲SfM流水线（如COLMAP）中的相机位姿估计。由此导致下游3DGS优化偏离正确场景几何。在NeRF-Synthetic基准上，插入12×12像素的补丁使重建误差在LPIPS指标上增加6.8倍，而中毒图像对人类观察者仍保持无害。PatchPoison无需修改现有流程，为内容创作者提供一种即插即用的多视角数据保护方案。
原文摘要 · Abstract (English)
3D Gaussian Splatting (3DGS) has recently enabled highly photorealistic 3D reconstruction from casually captured multi-view images. However, this accessibility raises a privacy concern: publicly available images or videos can be exploited to reconstruct detailed 3D models of scenes or objects without the owner's consent. We present PatchPoison, a lightweight dataset-poisoning method that prevents unauthorized 3D reconstruction. Unlike global perturbations, PatchPoison injects a small high-frequency adversarial patch, a structured checkerboard, into the periphery of each image in a multi-view dataset. The patch is designed to corrupt the feature-matching stage of Structure-from-Motion (SfM) pipelines such as COLMAP by introducing spurious correspondences that systematically misalign estimated camera poses. Consequently, downstream 3DGS optimization diverges from the correct scene geometry. On the NeRF-Synthetic benchmark, inserting a 12 X 12 pixel patch increases reconstruction error by 6.8x in LPIPS, while the poisoned images remain unobtrusive to human viewers. PatchPoison requires no pipeline modifications, offering a practical, "drop-in" preprocessing step for content creators to protect their multi-view data.
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