3D Gaussian Splatting has shown impressive novel view synthesis results; nonetheless, it is vulnerable to dynamic objects polluting the input data of an otherwise static scene, so called distractors. Distractors have severe impact on the rendering quality as they get represented as view-dependent effects or result in floating artifacts. Our goal is to identify and ignore such distractors during the 3D Gaussian optimization to obtain a clean reconstruction. To this end, we take a self-supervised approach that looks at the image residuals during the optimization to determine areas that have likely been falsified by a distractor. In addition, we leverage a pretrained segmentation network to provide object awareness, enabling more accurate exclusion of distractors. This way, we obtain segmentation masks of distractors to effectively ignore them in the loss formulation. We demonstrate that our approach is robust to various distractors and strongly improves rendering quality on distractor-polluted scenes, improving PSNR by 1.86dB compared to 3D Gaussian Splatting.

翻译：三维高斯溅射技术在新视角合成方面已展现出卓越性能；然而，该方法易受动态物体（即所谓干扰物）污染静态场景输入数据的影响。干扰物会以视角相关效应或悬浮伪影的形式被表征，对渲染质量造成严重影响。本研究旨在通过三维高斯优化过程识别并排除此类干扰物，从而获得洁净的重建结果。为此，我们采用自监督方法，通过分析优化过程中的图像残差来确定可能被干扰物篡改的区域。此外，我们利用预训练分割网络提供物体感知能力，实现更精确的干扰物排除。通过这种方式，我们获得干扰物的分割掩码，从而在损失函数中有效忽略这些区域。实验证明，该方法对各类干扰物具有鲁棒性，在受干扰物污染的场景中显著提升渲染质量，与原始三维高斯溅射方法相比，PSNR指标提升1.86dB。