论文标题
从少数图像中学习可通用的光场网络
Learning Generalizable Light Field Networks from Few Images
论文作者
论文摘要
我们探索了一种基于神经光场表示的几种新颖观点合成的新策略。给定目标摄像头姿势，隐式神经网络将每个射线直接映射到其目标像素的颜色。该网络的条件是根据来自显式3D特征量的粗量渲染产生的本地射线特征。该卷是由使用3D Convnet的输入图像构建的。我们的方法在基于最先进的神经辐射场竞争方面，在合成和真实MVS数据上实现了竞争性能，同时提供了更快的渲染速度。
We explore a new strategy for few-shot novel view synthesis based on a neural light field representation. Given a target camera pose, an implicit neural network maps each ray to its target pixel's color directly. The network is conditioned on local ray features generated by coarse volumetric rendering from an explicit 3D feature volume. This volume is built from the input images using a 3D ConvNet. Our method achieves competitive performances on synthetic and real MVS data with respect to state-of-the-art neural radiance field based competition, while offering a 100 times faster rendering.
