This paper presents the first unified distractor removal method, named IDDR-NGP, which directly operates on Instant-NPG. The method is able to remove a wide range of distractors in 3D scenes, such as snowflakes, confetti, defoliation and petals, whereas existing methods usually focus on a specific type of distractors. By incorporating implicit 3D representations with 2D detectors, we demonstrate that it is possible to efficiently restore 3D scenes from multiple corrupted images. We design the learned perceptual image patch similarity~( LPIPS) loss and the multi-view compensation loss (MVCL) to jointly optimize the rendering results of IDDR-NGP, which could aggregate information from multi-view corrupted images. All of them can be trained in an end-to-end manner to synthesize high-quality 3D scenes. To support the research on distractors removal in implicit 3D representations, we build a new benchmark dataset that consists of both synthetic and real-world distractors. To validate the effectiveness and robustness of IDDR-NGP, we provide a wide range of distractors with corresponding annotated labels added to both realistic and synthetic scenes. Extensive experimental results demonstrate the effectiveness and robustness of IDDR-NGP in removing multiple types of distractors. In addition, our approach achieves results comparable with the existing SOTA desnow methods and is capable of accurately removing both realistic and synthetic distractors.

翻译：本文提出了首个统一的干扰物移除方法IDDR-NGP，该方法直接在Instant-NGP上操作。该方法能够移除三维场景中的多种干扰物，例如雪花、彩屑、落叶和花瓣，而现有方法通常只针对特定类型的干扰物。通过将隐式三维表示与二维检测器相结合，我们证明了从多幅受损图像中高效重建三维场景是可行的。我们设计了学习感知图像块相似度（LPIPS）损失与多视角补偿损失（MVCL），共同优化IDDR-NGP的渲染结果，从而聚合来自多视角受损图像的信息。所有模块均可通过端到端训练合成高质量的三维场景。为支持隐式三维表示中干扰物移除的研究，我们构建了一个包含合成与真实世界干扰物的新基准数据集。为验证IDDR-NGP的有效性与鲁棒性，我们在真实场景与合成场景中添加了多种带有标注标签的干扰物。大量实验结果表明IDDR-NGP在移除多类干扰物方面具有显著的有效性与鲁棒性。此外，本方法取得了与现有最先进去雪方法相当的结果，并能准确移除真实与合成的干扰物。