论文标题
Autorf：从单视图观察到学习3D对象辐射字段
AutoRF: Learning 3D Object Radiance Fields from Single View Observations
论文作者
论文摘要
我们介绍Autorf-一种学习神经3D对象表示的新方法，其中仅通过一个视图观察到训练集中的每个对象。这种设置与大多数利用同一对象的多种视图，在培训期间采用明确的先验或需要像素完美注释的现有作品形成鲜明对比。为了解决这个具有挑战性的环境，我们建议学习一个归一化的，以对象为中心的表示，其嵌入描述和脱离形状，外观和姿势。每个编码都提供了有关感兴趣对象的通用，紧凑的信息，该信息以单次拍摄为新的目标视图，从而使新的视图合成。我们通过在测试时间优化形状和外观代码来进一步提高重建质量，通过将表示形式紧密地拟合到输入图像中。在一系列实验中，我们表明我们的方法很好地概括了看不见的对象，即使是在挑战现实世界的不同数据集中，例如Nuscenes，Kitti和Mapillary Metropolis。
We introduce AutoRF - a new approach for learning neural 3D object representations where each object in the training set is observed by only a single view. This setting is in stark contrast to the majority of existing works that leverage multiple views of the same object, employ explicit priors during training, or require pixel-perfect annotations. To address this challenging setting, we propose to learn a normalized, object-centric representation whose embedding describes and disentangles shape, appearance, and pose. Each encoding provides well-generalizable, compact information about the object of interest, which is decoded in a single-shot into a new target view, thus enabling novel view synthesis. We further improve the reconstruction quality by optimizing shape and appearance codes at test time by fitting the representation tightly to the input image. In a series of experiments, we show that our method generalizes well to unseen objects, even across different datasets of challenging real-world street scenes such as nuScenes, KITTI, and Mapillary Metropolis.
