We present a bottom-up differentiable relaxation of the process of drawing
points, lines and curves into a pixel raster. Our approach arises from the
observation that rasterising a pixel in an image given parameters of a
primitive can be reformulated in terms of the primitive's distance transform,
and then relaxed to allow the primitive's parameters to be learned. This
relaxation allows end-to-end differentiable programs and deep networks to be
learned and optimised and provides several building blocks that allow control
over how a compositional drawing process is modelled. We emphasise the
bottom-up nature of our proposed approach, which allows for drawing operations
to be composed in ways that can mimic the physical reality of drawing rather
than being tied to, for example, approaches in modern computer graphics. With
the proposed approach we demonstrate how sketches can be generated by directly
optimising against photographs and how auto-encoders can be built to transform
rasterised handwritten digits into vectors without supervision. Extensive
experimental results highlight the power of this approach under different
modelling assumptions for drawing tasks.

该论文提出了一种可微分方案，用于反距离变换松弛像素栅格的绘制过程，以学习和优化图像的绘制过程，支持自动编码器将栅格化的手写数字转换成向量。

可微绘画与素描

Differentiable Drawing and Sketching

There is a growing number of tasks that work directly on point clouds. As the
size of the point cloud grows, so do the computational demands of these tasks.
A possible solution is to sample the point cloud first. Classic sampling
approaches, such as farthest point sampling (FPS), do not consider the
downstream task. A recent work showed that learning a task-specific sampling
can improve results significantly. However, the proposed technique did not deal
with the non-differentiability of the sampling operation and offered a
workaround instead. We introduce a novel differentiable relaxation for point
cloud sampling that approximates sampled points as a mixture of points in the
primary input cloud. Our approximation scheme leads to consistently good
results on classification and geometry reconstruction applications. We also
show that the proposed sampling method can be used as a front to a point cloud
registration network. This is a challenging task since sampling must be
consistent across two different point clouds for a shared downstream task. In
all cases, our approach outperforms existing non-learned and learned sampling
alternatives. Our code is publicly available at
this https URL

本文介绍了一种新的差分松弛算法用于点云采样，请参考本文中的公式和实验结果，该方法在点云分类与几何重建等任务上表现优异。

SampleNet: 可微分点云采样

SampleNet: Differentiable Point Cloud Sampling

Non-local self-similarity and sparsity principles have proven to be powerful
priors for natural image modeling. We propose a novel differentiable relaxation
of joint sparsity that exploits both principles and leads to a general
framework for image restoration which is (1) trainable end to end, (2) fully
interpretable, and (3) much more compact than competing deep learning
architectures. We apply this approach to denoising, jpeg deblocking, and
demosaicking, and show that, with as few as 100K parameters, its performance on
several standard benchmarks is on par or better than state-of-the-art methods
that may have an order of magnitude or more parameters.

本文提出了一种新的可微分松弛联合稀疏性方法，利用非局部自相似性和稀疏性原则进行图像恢复，经实验证明该方法参数少，性能与现有先进方法相当或更好。