Line segments are powerful features complementary to points. They offer
structural cues, robust to drastic viewpoint and illumination changes, and can
be present even in texture-less areas. However, describing and matching them is
more challenging compared to points due to partial occlusions, lack of texture,
or repetitiveness. This paper introduces a new matching paradigm, where points,
lines, and their descriptors are unified into a single wireframe structure. We
propose GlueStick, a deep matching Graph Neural Network (GNN) that takes two
wireframes from different images and leverages the connectivity information
between nodes to better glue them together. In addition to the increased
efficiency brought by the joint matching, we also demonstrate a large boost of
performance when leveraging the complementary nature of these two features in a
single architecture. We show that our matching strategy outperforms the
state-of-the-art approaches independently matching line segments and points for
a wide variety of datasets and tasks. The code is available at
this https URL

本文提出一种新的匹配模式，将点、线及其描述符统一成一个单独的框架结构，并提出 GlueStick，一种深度匹配图神经网络，以便从不同的图像中取出两个线框并利用节点之间的连接信息更好地将它们粘合在一起。我们证明了这种匹配策略可以优于当前最先进的独立匹配线段和点的方法，适用于各种数据集和任务。

GlueStick: 基于点线粘合的鲁棒图像匹配

GlueStick: Robust Image Matching by Sticking Points and Lines Together

The key challenge in designing a sketch representation lies with handling the
abstract and iconic nature of sketches. Existing work predominantly utilizes
either, (i) a pixelative format that treats sketches as natural images
employing off-the-shelf CNN-based networks, or (ii) an elaborately designed
vector format that leverages the structural information of drawing orders using
sequential RNN-based methods. While the pixelative format lacks intuitive
exploitation of structural cues, sketches in vector format are absent in most
cases limiting their practical usage. Hence, in this paper, we propose a
lattice structured sketch representation that not only removes the bottleneck
of requiring vector data but also preserves the structural cues that vector
data provides. Essentially, sketch lattice is a set of points sampled from the
pixelative format of the sketch using a lattice graph. We show that our lattice
structure is particularly amenable to structural changes that largely benefits
sketch abstraction modeling for generation tasks. Our lattice representation
could be effectively encoded using a graph model, that uses significantly fewer
model parameters (13.5 times lesser) than existing state-of-the-art. Extensive
experiments demonstrate the effectiveness of sketch lattice for sketch
manipulation, including sketch healing and image-to-sketch synthesis.

本文提出了一种基于格点结构的草图表示方法，通过有效编码该表示方法，实现了比现有技术少出 13.5 倍模型参数的草图抽象模型生成，并准确描述了其功能和效果。

SketchLattice: 用于手绘操作的晶格表示

SketchLattice: Latticed Representation for Sketch Manipulation

In this paper, we propose to augment image-based place recognition with
structural cues. Specifically, these structural cues are obtained using
structure-from-motion, such that no additional sensors are needed for place
recognition. This is achieved by augmenting the 2D convolutional neural network
(CNN) typically used for image-based place recognition with a 3D CNN that takes
as input a voxel grid derived from the structure-from-motion point cloud. We
evaluate different methods for fusing the 2D and 3D features and obtain best
performance with global average pooling and simple concatenation. On the Oxford
RobotCar dataset, the resulting descriptor exhibits superior recognition
performance compared to descriptors extracted from only one of the input
modalities, including state-of-the-art image-based descriptors. Especially at
low descriptor dimensionalities, we outperform state-of-the-art descriptors by
up to 90%.

在这篇论文中，我们提出了使用结构线索来增强基于图像的位置识别，这是通过利用结构运动获得的，因此不需要为位置识别使用任何其他传感器，这是通过将基于图像的位置识别通常使用的 2D 卷积神经网络与从结构运动点云导出的体素网格作为输入的 3D CNN 相结合来完成的。我们评估了不同的方法来融合 2D 和 3D 特征，并通过全局平均汇集和简单的串联获得了最佳性能。在牛津机器人车数据集上，得到的描述符表现优于仅从一个输入模态性提取的描述符，包括最先进的基于图像的描述符。特别是在低描述符维度下，我们的表现超过最先进的描述符高达 90％。