超分辨率matlab代码小波域样式转移在单图像超分辨率中实现有效的感知失真权衡 ICCV 2019口头报告可以从以下链接下载不同数据集的结果： 这是ICCV论文“在单图像超分辨率中有效实现感知失真权衡的小波域样式转换”的实现。 第一步，您需要运行SWT.m来生成LL子带和六个高频子带。 第二步，请将上面生成的高频子带分别复制到Content和Style文件中，然后运行command.sh进行小波域风格传输。 可以从下载VGG文件，然后将其放在pre-trained_model文件中。 在第三步骤中，通过VDSR网络进一步增强了在第一步骤中生成的LL子带。 注意，需要针对不同的小波滤波器对网络进行重新训练。 所提供的模型仅适用于哈尔过滤器。 在第四步中，通过运行ISWT.m，使用逆SWT将生成的LL和高频子带重新组合为图像。 样式传送软件代码基于进行了修改。 NRQM分数是使用PIRM挑战提供的matlab代码计算的。

Factor Graphs for Robot Perception.pdf Factor Graphs for Robot Perception.pdf Factor Graphs for Robot Perception.pdf Factor Graphs for Robot Perception.pdf Factor Graphs for Robot Perception.pdf Factor Graphs for Robot Perception.pdf

We review the use of factor graphs for the modeling and solving of large-scale inference problems in robotics. Factor graphs are a family of probabilistic graphical models, other examples of which are Bayesian networks and Markov random fields, well known from the statistical modeling and machine learning literature. They provide a powerful abstraction that gives insight into particular inference problems, making it easier to think about and design solutions, and write modular software to perform the actual inference. We illustrate their use in the simultaneous localization and mapping problem and other important problems associated with deploying robots in the real world. We introduce factor graphs as an economical representation within which to formulate the different inference problems, setting the stage for the subsequent sections on practical methods to solve them.We explain the nonlinear optimization techniques for solving arbitrary nonlinear factor graphs, which requires repeatedly solving large sparse linear systems. The sparse structure of the factor graph is the key to understanding this more general algorithm, and hence also understanding (and improving) sparse factorization methods. We provide insight into the graphs underlying robotics inference, and how their sparsity is affected by the implementation choices we make, crucial for achieving highly performant algorithms. As many inference problems in robotics are incremental, we also discuss the iSAM class of algorithms that can reuse previous computations, re-interpreting incremental matrix factorization methods as operations on graphical models, introducing the Bayes tree in the process. Because in most practical situations we will have to deal with 3D rotations and other nonlinear manifolds, we also introduce the more sophisticated machinery to perform optimization on nonlinear manifolds. Finally, we provide an overview of applications of factor graphs for robot perception, showing the broad impact fa

《Modeling Color Difference for Visualization Design》，该论文是2017年的一篇最佳会议论文，对可视化中的色差进行建模，比较有创新性，作为图形学课程报告，由于我专业不是图形方向，对这个不太了解，一开始入手很难理解，花了几天啃出来的重要内容都在报告中说明了~

Factor Graphs for Robot Perception.pdf

baidu apollo project doc v5.5.0

Baidu Apollo: Perception of Low-Cost Autonomous Driving

object_recognition 3D物体识别相关包

car-perception-master车辆识别开源程序 python开发

机器人感知 该存储库展示了马里兰大学ENPM673课程中完成的项目。 本课程专门设计用于提供对机器人感知的见解，包括从最基本的主题（例如各种图像转换）到最新的算法（例如单眼视觉测距法）。 在本节中，将简要介绍本课程中完成的每个项目。 下面列出了本课程中完成的项目，请单击链接进入特定部分： 请注意，所有代码均已在MATLAB 2016b和MATLAB 2017a上进行了测试和运行。 还要注意，对于视觉里程表项目，您将需要Mathworks Inc.提供的MATLAB的Computer Vision工具箱。可以在找到。 增强现实 这是使用非常基本的概念（例如图像变换和单应性）来生成非常强大的结果（