论文标题
GRF：学习3D表示和渲染的一般光辉领域
GRF: Learning a General Radiance Field for 3D Representation and Rendering
论文作者
论文摘要
我们提出了一个简单而强大的神经网络，该网络仅来自2D观测值暗中代表和渲染3D对象和场景。网络将3D几何形状作为通用辐射场模型，将一组带有相机姿势和内在的2D图像作为输入，为3D空间的每个点构造了内部表示，然后呈现从任意位置观察的该点的相应外观和几何形状。我们方法的关键是要在2D图像中学习每个像素的本地特征，然后将这些功能投影到3D点，从而产生一般和丰富的点表示。我们还将注意力机制集成到来自多个2D视图的汇总像素特征，从而隐性地考虑了视觉遮挡。广泛的实验表明，我们的方法可以为新颖对象，看不见的类别和具有挑战性的现实世界而产生高质量和现实的新颖观点。
We present a simple yet powerful neural network that implicitly represents and renders 3D objects and scenes only from 2D observations. The network models 3D geometries as a general radiance field, which takes a set of 2D images with camera poses and intrinsics as input, constructs an internal representation for each point of the 3D space, and then renders the corresponding appearance and geometry of that point viewed from an arbitrary position. The key to our approach is to learn local features for each pixel in 2D images and to then project these features to 3D points, thus yielding general and rich point representations. We additionally integrate an attention mechanism to aggregate pixel features from multiple 2D views, such that visual occlusions are implicitly taken into account. Extensive experiments demonstrate that our method can generate high-quality and realistic novel views for novel objects, unseen categories and challenging real-world scenes.
