In this paper we present ADOP, a novel point-based, differentiable neural
rendering pipeline. Like other neural renderers, our system takes as input
calibrated camera images and a proxy geometry of the scene, in our case a point
cloud. To generate a novel view, the point cloud is rasterized with learned
feature vectors as colors and a deep neural network fills the remaining holes
and shades each output pixel. The rasterizer renders points as one-pixel
splats, which makes it very fast and allows us to compute gradients with
respect to all relevant input parameters efficiently. Furthermore, our pipeline
contains a fully differentiable physically-based photometric camera model,
including exposure, white balance, and a camera response function. Following
the idea of inverse rendering, we use our renderer to refine its input in order
to reduce inconsistencies and optimize the quality of its output. In
particular, we can optimize structural parameters like the camera pose, lens
distortions, point positions and features, and a neural environment map, but
also photometric parameters like camera response function, vignetting, and
per-image exposure and white balance. Because our pipeline includes photometric
parameters, e.g.~exposure and camera response function, our system can smoothly
handle input images with varying exposure and white balance, and generates
high-dynamic range output. We show that due to the improved input, we can
achieve high render quality, also for difficult input, e.g. with imperfect
camera calibrations, inaccurate proxy geometry, or varying exposure. As a
result, a simpler and thus faster deep neural network is sufficient for
reconstruction. In combination with the fast point rasterization, ADOP achieves
real-time rendering rates even for models with well over 100M points.
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本文提出了 ADOP，一个新颖的基于点的可微分神经渲染管道，并展示了它能够实现高渲染质量和实时渲染速度，包含光度参数（曝光，白平衡和响应函数）以及结构参数（如相机位姿，镜头畸变，点位置等）。

ADOP：近似可微分的一像素点渲染

ADOP: Approximate Differentiable One-Pixel Point Rendering

We present a method to estimate lighting from a single image of an indoor
scene. Previous work has used an environment map representation that does not
account for the localized nature of indoor lighting. Instead, we represent
lighting as a set of discrete 3D lights with geometric and photometric
parameters. We train a deep neural network to regress these parameters from a
single image, on a dataset of environment maps annotated with depth. We propose
a differentiable layer to convert these parameters to an environment map to
compute our loss; this bypasses the challenge of establishing correspondences
between estimated and ground truth lights. We demonstrate, via quantitative and
qualitative evaluations, that our representation and training scheme lead to
more accurate results compared to previous work, while allowing for more
realistic 3D object compositing with spatially-varying lighting.

提出了一种利用深度神经网络从单张室内场景图片中估算光照的方法，利用离散化的 3D 灯光几何和光度参数进行表征，并证明该方法比以往方法更加精确并能支持空间变化光照的三维物体合成。