Originally designed for applications in computer graphics, visual computing (VC) methods synthesize information about physical and virtual worlds, using prescribed algorithms optimized for spatial computing. VC is used to analyze geometry, physically simulate solids, fluids, and other media, and render the world via optical techniques. These fine-tuned computations that operate explicitly on a given input solve so-called forward problems, VC excels at. By contrast, deep learning (DL) allows for the construction of general algorithmic models, side stepping the need for a purely first principles-based approach to problem solving. DL is powered by highly parameterized neural network architectures -- universal function approximators -- and gradient-based search algorithms which can efficiently search that large parameter space for optimal models. This approach is predicated by neural network differentiability, the requirement that analytic derivatives of a given problem's task metric can be computed with respect to neural network's parameters. Neural networks excel when an explicit model is not known, and neural network training solves an inverse problem in which a model is computed from data.

通过使用计算机图形学 （Computer Graphics, CG）技术，视觉计算（Visual Computing, VC）方法通过经过优化的算法综合了有关物理世界和虚拟世界的信息，用于分析几何结构、模拟物体、流体和其他媒体，并通过光学技术渲染世界。与之相对，深度学习（Deep Learning, DL）通过使用高度参数化的神经网络架构和基于梯度的搜索算法来构建通用算法模型，避开了纯粹基于第一原理的问题解决方法的需要。当缺乏明确模型时，神经网络表现出色，并且通过神经网络训练解决了一个由数据计算模型的逆问题。

可微可视计算用于逆问题和机器学习