PatchPoison: Poisoning Multi-View Datasets to Degrade 3D Reconstruction

Poisoning input views of 3D reconstruction systems has been recently studied. However, we identify that 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 3D reconstruction pipeline. In this paper, we argue that the structure-from-motion (SfM) initialization, as the geometric core of many widely used reconstruction systems, can be targeted to achieve transferable poisoning effects across diverse 3D reconstruction systems. 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, eventually resulting in low-quality rendered views. We also provide a theoretical analysis that connects cross-view inconsistency to correspondence collapse. Experimental results demonstrate the effectiveness of our PoInit-of-View on diverse 3D reconstruction systems and datasets, surpassing the single-view baseline by 25.1% in PSNR and 16.5% in SSIM in black-box transfer settings, such as 3DGS to NeRF.

3D Gaussian Splatting (3DGS) delivers high-fidelity real-time rendering but suffers from geometric and photometric degradations under sparse-view constraints. Current generative restoration approaches are often limited by insufficient temporal coherence, a lack of explicit spatial constraints, and a lack of large-scale training data, resulting in multi-view inconsistencies, erroneous geometric hallucinations, and limited generalization to diverse real-world artifact distributions. In this paper, we present ArtifactWorld, a framework that resolves 3DGS artifact repair through systematic data expansion and a homogeneous dual-model paradigm. To address the data bottleneck, we establish a fine-grained phenomenological taxonomy of 3DGS artifacts and construct a comprehensive training set of 107.5K diverse paired video clips to enhance model robustness. Architecturally, we unify the restoration process within a video diffusion backbone, utilizing an isomorphic predictor to localize structural defects via an artifact heatmap. This heatmap then guides the restoration through an Artifact-Aware Triplet Fusion mechanism, enabling precise, intensity-guided spatio-temporal repair within native self-attention. Extensive experiments demonstrate that ArtifactWorld achieves state-of-the-art performance in sparse novel view synthesis and robust 3D reconstruction. Code available at: https://github.com/fyting/ArtifactWorld.

A pretrained 3D Gaussian Splatting (3DGS) reconstruction can reproduce its capture views well while retaining off-object structure that becomes visible under reframing. We present CleanGS, a post-hoc cleanup method that removes such structure without retraining the underlying representation. Given calibrated views and binary target masks, CleanGS scores candidate three-view subsets using mask reliability, camera baseline, viewing-direction diversity, and complementary sparse 3D support. Rather than collapsing these objectives a priori, it computes the exact Pareto front over subsets of the reduced candidate pool and selects a balanced on-dominated subset using weighted Chebyshev scalarization. The selected views then drive rasterization-aware two-of-three voting to retain a subset of the original Gaussians. We evaluate the exported asset directly and introduce a fixed-pose widened-field protocol that exposes structure outside the original framing. Across 18 scenes, CleanGS achieves 19.03 dB standard and 17.83 dB widened-field PSNR, compared with 16.44/16.29 dB for FlashSplat and 15.28/15.10 dB for GaussianCut with the same three masks. CleanGS leads five of six photometric metrics and remains within 0.32 dB of the uncleaned reconstruction in standard PSNR. Its filtering-and-export stage takes a median of 20 seconds and 1.9 GB peak memory, compared with 3.1 minutes and 41.6 GB for FlashSplat and 4.2 minutes and 34.9 GB for GaussianCut. Code is available at https://github.com/smlab-niser/clean-gs/.