PoInit-of-View: Poisoning Initialization of Views Transfers Across Multiple 3D Reconstruction Systems

Weijie Wang, Songlong Xing, Zhengyu Zhao, Nicu Sebe, Bruno Lepri; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026, pp. 20670-20679

Poisoning input views of 3D reconstruction systems has been recently studied. However, existing studies simply backpropagate adversarial gradients through the 3D reconstruction pipeline as a whole, without uncovering the new vulnerability rooted in specific modules of the pipeline. In this paper, we argue that structure-from-motion (SfM), as the geometric core of many widely used reconstruction systems, can be targeted to achieve strong poisoning effects. To this end, we propose PoInit-of-View, which optimizes adversarial perturbations to intentionally introduce cross-view gradient inconsistencies at projections of corresponding 3D points. These inconsistencies disrupt keypoint detection and feature matching, thereby corrupting pose estimation and triangulation within SfM and eventually resulting in low-quality rendered views. We also provide a theoretical analysis connecting cross-view inconsistency to correspondence collapse. Experimental results demonstrate the effectiveness of PoInit-of-View on diverse 3D reconstruction systems and datasets, surpassing the single-view-based method by 25.1% percent in PSNR and 16.5% percent in SSIM in black-box transfer settings, such as 3DGS to NeRF.