论文标题
从神经辐射场中删除对象
Removing Objects From Neural Radiance Fields
论文作者
论文摘要
神经辐射场（NERF）正在成为无处不在的场景表示，可以进行新的视图综合。 Nerf越来越多地与其他人共享。但是，在共享NERF之前，可能需要删除个人信息或难看的对象。当前的NERF编辑框架不容易实现此类删除。我们提出了一个框架，以从RGB-D序列创建的NERF表示中删除对象。我们的NERF介绍方法利用了2D图像插入的最新工作，并由用户提供的掩码进行指导。我们的算法以基于置信度的视图选择程序为基础。它选择用于创建NERF的单个2D插图图像中的哪个，以便由此产生的Indeded Nerf是3D一致的。我们表明，我们的NERF编辑方法可有效以多视图相干方式合成合理的绘制。我们使用新的且仍在挑战的数据集来验证我们的方法，以实现NERF授课的任务。
Neural Radiance Fields (NeRFs) are emerging as a ubiquitous scene representation that allows for novel view synthesis. Increasingly, NeRFs will be shareable with other people. Before sharing a NeRF, though, it might be desirable to remove personal information or unsightly objects. Such removal is not easily achieved with the current NeRF editing frameworks. We propose a framework to remove objects from a NeRF representation created from an RGB-D sequence. Our NeRF inpainting method leverages recent work in 2D image inpainting and is guided by a user-provided mask. Our algorithm is underpinned by a confidence based view selection procedure. It chooses which of the individual 2D inpainted images to use in the creation of the NeRF, so that the resulting inpainted NeRF is 3D consistent. We show that our method for NeRF editing is effective for synthesizing plausible inpaintings in a multi-view coherent manner. We validate our approach using a new and still-challenging dataset for the task of NeRF inpainting.
