Unsupervised learning with generative models has the potential of discovering
rich representations of 3D scenes. While geometric deep learning has explored
3D-structure-aware representations of scene geometry, these models typically
require explicit 3D supervision. Emerging neural scene representations can be
trained only with posed 2D images, but existing methods ignore the
three-dimensional structure of scenes. We propose Scene Representation Networks
(SRNs), a continuous, 3D-structure-aware scene representation that encodes both
geometry and appearance. SRNs represent scenes as continuous functions that map
world coordinates to a feature representation of local scene properties. By
formulating the image formation as a differentiable ray-marching algorithm,
SRNs can be trained end-to-end from only 2D images and their camera poses,
without access to depth or shape. This formulation naturally generalizes across
scenes, learning powerful geometry and appearance priors in the process. We
demonstrate the potential of SRNs by evaluating them for novel view synthesis,
few-shot reconstruction, joint shape and appearance interpolation, and
unsupervised discovery of a non-rigid face model.

提出了一种连续三维结构感知场景表征模型 Scene Representation Networks (SRNs)，能够通过不需要深度或形状信息的二维图像及其相机姿态进行端到端训练， 并在视角合成、少样本重建、形状外插及表现插值等任务中展示出潜力。