论文标题
可区分的渲染：调查
Differentiable Rendering: A Survey
论文作者
论文摘要
深度神经网络（DNN）在与视觉相关的任务（例如对象检测或图像分割）上显示出显着的性能改进。尽管他们取得了成功，但他们通常缺乏对形成图像的3D对象的理解，因为并非总是有可能收集有关场景的3D信息或轻松注释它。可区分的渲染是一个新的领域，可以通过图像计算和传播3D对象的梯度。它还减少了3D数据收集和注释的需求，同时在各种应用程序中实现了更高的成功率。本文回顾了现有文献，并讨论了可区分渲染，其应用和开放研究问题的当前状态。
Deep neural networks (DNNs) have shown remarkable performance improvements on vision-related tasks such as object detection or image segmentation. Despite their success, they generally lack the understanding of 3D objects which form the image, as it is not always possible to collect 3D information about the scene or to easily annotate it. Differentiable rendering is a novel field which allows the gradients of 3D objects to be calculated and propagated through images. It also reduces the requirement of 3D data collection and annotation, while enabling higher success rate in various applications. This paper reviews existing literature and discusses the current state of differentiable rendering, its applications and open research problems.
