Inferring a meaningful geometric scene representation from a single image is
a fundamental problem in computer vision. Approaches based on traditional depth
map prediction can only reason about areas that are visible in the image.
Currently, neural radiance fields (NeRFs) can capture true 3D including color,
but are too complex to be generated from a single image. As an alternative, we
propose to predict implicit density fields. A density field maps every location
in the frustum of the input image to volumetric density. By directly sampling
color from the available views instead of storing color in the density field,
our scene representation becomes significantly less complex compared to NeRFs,
and a neural network can predict it in a single forward pass. The prediction
network is trained through self-supervision from only video data. Our
formulation allows volume rendering to perform both depth prediction and novel
view synthesis. Through experiments, we show that our method is able to predict
meaningful geometry for regions that are occluded in the input image.
Additionally, we demonstrate the potential of our approach on three datasets
for depth prediction and novel-view synthesis.

该研究提出使用隐式密度场而非神经辐射场作为图像的几何场景表示，通过自监督训练神经网络能够在单个前向传递中预测该场景表示，并且可以在深度预测和新视角合成方面进行体积渲染。实验表明，该方法能够预测出输入图像中遮挡区域的有意义几何信息。