发表机构
Digital Future Lab, Flanders Make, Hasselt University（数字未来实验室，弗拉芒制造，哈塞尔特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对3D高斯点渲染中屏幕空间伪影问题，提出SSA-3DGS无监督框架，利用视图几何一致性，联合优化3D场景与2D叠加，在合成及真实数据集上，相比同条件3DGS提高重建保真度9dB PSNR，且保留伪影。
AI 中文摘要
新颖视图合成（NVS）方法，如3D高斯点渲染（3DGS），严重依赖于干净、多视图一致、已姿态化的输入图像这一假设。现实世界捕获可能因屏幕空间伪影——固定在2D图像平面而非3D世界的静态遮挡——而违反此假设。常见例子包括物理传感器缺陷、环境障碍物、捕获障碍物和数字叠加。本文提出SSA-3DGS，一个无监督框架，联合优化3D场景和可学习的2D叠加以恢复干净的3D场景和损坏伪影。通过利用视图间的几何一致性，该方法无需监督或手动输入就能有效从3D场景几何中分离静态伪影。在各种合成损坏和自捕获的真实世界数据集上，SSA-3DGS比在相同损坏输入上训练的3DGS提高重建保真度高达9dB PSNR，同时忠实地保留损坏伪影。
英文摘要
Novel View Synthesis (NVS) methods, such as 3D Gaussian Splatting (3DGS), rely on the assumption of clean, multi-view consistent, posed input images. Real-world captures can violate this assumption due to \textbf{screen-space artifacts}---static occlusions fixed to the 2D image plane rather than to the 3D world. Common examples include physical sensor defects, environmental obstructions (such as rain or mud on the lens enclosure), capture obstructions (such as a thumb over the camera sensor or a dashboard visible in dashcam footage), and digital overlays (such as watermarks or UI elements). When present, they are erroneously baked into the 3D geometry as ``floaters'' or near-camera artifacts, degrading the quality of novel-view rendering. In this work, we propose \textit{SSA-3DGS}, an unsupervised framework that jointly optimizes a 3D scene and a learnable 2D overlay to recover a clean 3D scene and the corrupting artifacts. By exploiting geometric consensus across views, our method effectively disentangles static artifacts from the 3D scene geometry without supervision or manual input. Across diverse synthetic corruptions and a self-captured real-world dataset, SSA-3DGS improves reconstruction fidelity by up to ${\sim}8$~dB PSNR over 3DGS trained on the same corrupted inputs, while faithfully preserving the corrupting artifact.
