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. https://github.com/darglein/ADOP
翻译:在本文中,我们展示了ADOP, 这是一种新型的基于点的、可差异的神经转换管道。 与其他神经转换器一样, 我们的系统以输入校正的相机图像和代理性对景色的几何模型, 在我们的情况中, 是一个点云, 产生一个新颖的视图。 为了生成新颖的视图, 点云被以学习的特质矢量来分解, 作为颜色和深层神经网络来填充其余的洞洞和每个输出像素的阴影。 特别是, 光化器将点变成一个点, 使得它非常快速, 使我们能够根据所有相关的输入参数来计算梯度。 此外, 我们的管道包含完全不同的物理光度光度摄像相机模型模型模型模型, 包括曝光量、 白平衡。 我们的光化和光化模型, 也能够实现高水平的摄像效果和平衡。