论文标题
神经体积对象选择
Neural Volumetric Object Selection
论文作者
论文摘要
我们介绍了一种在神经体积3D表示中选择对象的方法，例如多平面图像（MPI）和神经辐射场（NERF）。我们的方法将一组前景和背景2D用户在一个视图中涂抹，并自动估计所需对象的3D分割，可以将其呈现为新颖的视图。为了实现这一结果，我们提出了一种新颖的体素特征嵌入，该特征嵌入了所有输入视图中的神经体积3D表示和多视图图像特征。为了评估我们的方法，我们介绍了一个新的人提供的分割掩码数据集，以在现实世界中多视频场景中捕获的对象。我们表明，我们的方法超出了强大的基准，包括适合我们任务的2D细分和3D分割方法。
We introduce an approach for selecting objects in neural volumetric 3D representations, such as multi-plane images (MPI) and neural radiance fields (NeRF). Our approach takes a set of foreground and background 2D user scribbles in one view and automatically estimates a 3D segmentation of the desired object, which can be rendered into novel views. To achieve this result, we propose a novel voxel feature embedding that incorporates the neural volumetric 3D representation and multi-view image features from all input views. To evaluate our approach, we introduce a new dataset of human-provided segmentation masks for depicted objects in real-world multi-view scene captures. We show that our approach out-performs strong baselines, including 2D segmentation and 3D segmentation approaches adapted to our task.
