We propose BokehMe, a hybrid bokeh rendering framework that marries a neural
renderer with a classical physically motivated renderer. Given a single image
and a potentially imperfect disparity map, BokehMe generates high-resolution
photo-realistic bokeh effects with adjustable blur size, focal plane, and
aperture shape. To this end, we analyze the errors from the classical
scattering-based method and derive a formulation to calculate an error map.
Based on this formulation, we implement the classical renderer by a
scattering-based method and propose a two-stage neural renderer to fix the
erroneous areas from the classical renderer. The neural renderer employs a
dynamic multi-scale scheme to efficiently handle arbitrary blur sizes, and it
is trained to handle imperfect disparity input. Experiments show that our
method compares favorably against previous methods on both synthetic image data
and real image data with predicted disparity. A user study is further conducted
to validate the advantage of our method.

BokehMe 是一种混合 bokeh 渲染框架，使用神经渲染器和经典物理驱动渲染器相结合，可以生成高分辨率的逼真 bokeh 效果，并提出了一种能够计算误差图的公式，实现了经典渲染器和一个两阶段神经渲染器联合起来矫正渲染误差。

当神经渲染遇上经典渲染：BokehMe

BokehMe: When Neural Rendering Meets Classical Rendering

Discrete optimal transport solvers do not scale well on dense large problems
since they do not explicitly exploit the geometric structure of the cost
function. In analogy to continuous optimal transport we provide a framework to
verify global optimality of a discrete transport plan locally. This allows
construction of an algorithm to solve large dense problems by considering a
sequence of sparse problems instead. The algorithm lends itself to being
combined with a hierarchical multi-scale scheme. Any existing discrete solver
can be used as internal black-box.Several cost functions, including the noisy
squared Euclidean distance, are explicitly detailed. We observe a significant
reduction of run-time and memory requirements.

提供了一个与连续的最优传输类比的框架，以便于在本地验证离散传输计划的全局最优性，从而构建一种通过考虑一系列稀疏问题来解决大规模密集问题的算法，进而可以与分层多尺度方案相结合，明显减少了运行时间和内存要求。