论文标题
X线：隐式神经视图 - 光图和时间图像插值
X-Fields: Implicit Neural View-, Light- and Time-Image Interpolation
论文作者
论文摘要
我们建议代表一组X-Field-a在不同视图，时间或照明条件下拍摄的2D图像，即视频，光场，反射场，反射场或组合，以学习神经网络（NN），以将其视图，时间或光坐标映射到2D图像。在新坐标处执行此NN会导致联合视图，时间或光插值。使这种可行的关键思想是一个已经知道图形（照明，3D投影，遮挡）的“基本技巧”的NN。 NN表示该渲染作为隐式图的输入，即在任何视图，时间或光坐标中，对于任何像素，如果视图，时间或光坐标变化，则可以量化其移动方式（像素位置相对于视图，时间，照明等）。我们的X场表示在几分钟之内进行了一个场景训练，从而导致一组紧凑的可训练参数，从而在观察，时间和照明的情况下进行实时导航。
We suggest to represent an X-Field -a set of 2D images taken across different view, time or illumination conditions, i.e., video, light field, reflectance fields or combinations thereof-by learning a neural network (NN) to map their view, time or light coordinates to 2D images. Executing this NN at new coordinates results in joint view, time or light interpolation. The key idea to make this workable is a NN that already knows the "basic tricks" of graphics (lighting, 3D projection, occlusion) in a hard-coded and differentiable form. The NN represents the input to that rendering as an implicit map, that for any view, time, or light coordinate and for any pixel can quantify how it will move if view, time or light coordinates change (Jacobian of pixel position with respect to view, time, illumination, etc.). Our X-Field representation is trained for one scene within minutes, leading to a compact set of trainable parameters and hence real-time navigation in view, time and illumination.
