论文标题
viinter：查看图像的隐式神经表示插值
VIINTER: View Interpolation with Implicit Neural Representations of Images
论文作者
论文摘要
我们提出VIINTER，这是一种通过插值插值的方法来查看插值的方法。我们利用与每个图像关联的学习的代码向量并在这些代码之间插值来实现观点转换。我们提出了几种可显着提高插值质量的技术。 VIINTER表示在不构造3D结构，估算摄像头或计算像素对应的情况下实现视图插值的新方法。我们验证了Viinter在具有不同类型的相机布局和场景组成的多个多视图场景上的有效性。随着图像的开发（与表面或体积相对）的焦点是诸如图像拟合和超分辨率之类的任务，我们展示了其查看插值的能力，并为将INR用于图像操纵任务提供了有希望的前景。
We present VIINTER, a method for view interpolation by interpolating the implicit neural representation (INR) of the captured images. We leverage the learned code vector associated with each image and interpolate between these codes to achieve viewpoint transitions. We propose several techniques that significantly enhance the interpolation quality. VIINTER signifies a new way to achieve view interpolation without constructing 3D structure, estimating camera poses, or computing pixel correspondence. We validate the effectiveness of VIINTER on several multi-view scenes with different types of camera layout and scene composition. As the development of INR of images (as opposed to surface or volume) has centered around tasks like image fitting and super-resolution, with VIINTER, we show its capability for view interpolation and offer a promising outlook on using INR for image manipulation tasks.
