We study the problem of extracting correspondences between a pair of point
clouds for registration. For correspondence retrieval, existing works benefit
from matching sparse keypoints detected from dense points but usually struggle
to guarantee their repeatability. To address this issue, we present CoFiNet -
Coarse-to-Fine Network which extracts hierarchical correspondences from coarse
to fine without keypoint detection. On a coarse scale and guided by a weighting
scheme, our model firstly learns to match down-sampled nodes whose vicinity
points share more overlap, which significantly shrinks the search space of a
consecutive stage. On a finer scale, node proposals are consecutively expanded
to patches that consist of groups of points together with associated
descriptors. Point correspondences are then refined from the overlap areas of
corresponding patches, by a density-adaptive matching module capable to deal
with varying point density. Extensive evaluation of CoFiNet on both indoor and
outdoor standard benchmarks shows our superiority over existing methods.
Especially on 3DLoMatch where point clouds share less overlap, CoFiNet
significantly outperforms state-of-the-art approaches by at least 5% on
Registration Recall, with at most two-third of their parameters.

本文介绍了一种名为 CoFiNet 的粗糙到精细网络技术，该技术无需进行关键点检测，而是通过权重方案从粗糙到精细地提取点云之间的对应关系并进行匹配。在标准测试中，CoFiNet 在室内和室外场景上都比现有方法表现优越，特别是在 3DLoMatch 上，其 Registration Recall 性能提高至少 5％，而参数至多只有现有方法的三分之二。

CoFiNet：可靠的粗到细的对应关系用于鲁棒的点云配准

CoFiNet: Reliable Coarse-to-fine Correspondences for Robust Point Cloud  Registration

Capturing images under extremely low-light conditions poses significant
challenges for the standard camera pipeline. Images become too dark and too
noisy, which makes traditional enhancement techniques almost impossible to
apply. Recently, learning-based approaches have shown very promising results
for this task since they have substantially more expressive capabilities to
allow for improved quality. Motivated by these studies, in this paper, we aim
to leverage burst photography to boost the performance and obtain much sharper
and more accurate RGB images from extremely dark raw images. The backbone of
our proposed framework is a novel coarse-to-fine network architecture that
generates high-quality outputs progressively. The coarse network predicts a
low-resolution, denoised raw image, which is then fed to the fine network to
recover fine-scale details and realistic textures. To further reduce the noise
level and improve the color accuracy, we extend this network to a permutation
invariant structure so that it takes a burst of low-light images as input and
merges information from multiple images at the feature-level. Our experiments
demonstrate that our approach leads to perceptually more pleasing results than
the state-of-the-art methods by producing more detailed and considerably higher
quality images.

本文提出了一种新的粗到细的神经网络架构，通过采用多张暗光照片进行融合并利用学习来处理图像，从而在极低光条件下提高图像质量