从单个2D图像生成3D模型而无需渲染的有效损失函数 | 诺维萨德大学剑桥大学 引文 如果您发现此代码对您的研究有用，请引用我们的论文。 @article{zubic2021effective, title={An Effective Loss Function for Generating 3D Models from Single 2D Image without Rendering}, author={Zubi{\'c}, Nikola and Li{\`o}, Pietro}, journal={arXiv preprint arXiv:2103.03390}, year={2021} } 先决条件 下载代码： Git使用以下命令克隆代码： git clone https://github.com/NikolaZubic/2dimageto3dmodel.git

全景图 该存储库包含多个图像拼接的实现。 高分辨率图像处理缓慢。 图像必须按从左到右的顺序提供，反之亦然。 演示->>> 要求 python3.7.1 的opencv3.4.2 或使用opencv-contrib-python，因为某些非免费功能不可用 pip install opencv-contrib-python==3.4.2.17 --force-reinstall 您将需要安装一些软件包： 麻木 matplotlib 项目结构： |_ main.py |_Step_By_Step.ipynb |_ utils.py |_ features.py |_ stitch.py | |_ data - | | |_ myhouse | | |- 001.jpg | | |- 002.jpg | |_ BK | | |

The seeds for this book were first planted in 2001 when Steve Seitz at the University of Wash- ington invited me to co-teach a course called “Computer Vision for Computer Graphics”. At that time, computer vision techniques were increasingly being used in computer graphics to create image-based models of real-world objects, to create visual effects, and to merge real- world imagery using computational photography techniques. Our decision to focus on the applications of computer vision to fun problems such as image stitching and photo-based 3D modeling from personal photos seemed to resonate well with our students.

计算机视觉算法与应用英文版，非中文版，请看好再下载

使用3D ResNet进行视频分类 这是使用训练的3D ResNet进行视频（动作）分类的pytorch。 在Kinetics数据集上训练了3D ResNet，该数据集包括400个动作类。 此代码将视频用作输入，并在得分模式下输出每16帧的班级名称和预测班级得分。 在功能模式下，此代码每16帧输出512个暗角的功能（在全局平均池化之后）。 提供此代码的Torch（Lua）版本。 要求 conda install pytorch torchvision cuda80 -c soumith FFmpeg，FFprobe wget http://johnvansickle.com/ffmpeg/

Concise Computer Vision - An Introduction into Theory and Algorithm

Product Description This book introduces the geometry of 3-D vision, that is, the reconstruction of 3-D models of objects from a collection of 2-D images. It details the classic theory of two view geometry and shows that a more proper tool for studying the geometry of multiple views is the so-called rank consideration of the multiple view matrix. It also develops practical reconstruction algorithms and discusses possible extensions of the theory. Review From the reviews: "Computer vision is invading our daily lives … . Covering all the aspects would be too vast an area to cover in one book, so here, the authors concentrated on the specific goal of recovering the geometry of a 3D object … . The 22 pages of references form a good guide to the literature. The authors found an excellent balance between a thorough mathematical treatment and the applications themselves. … the text will be a pleasure to read for students … ." (Adhemar Bultheel, Bulletin of the Belgian Mathematical Society, Vol. 12 (2), 2005) "This is primarily a textbook of core principles, taking the reader from the most basic concepts of machine vision … to detailed applications, such as autonomous vehicle navigation. … It is a clearly written book … . Everything that is required is introduced … . an entirely self-contained work. … The book is aimed at graduate or advanced undergraduate students in electrical engineering, computer science, applied mathematics, or indeed anyone interested in machine vision … . is highly recommended." (D.E. Holmgren, The Photogrammetric Record, 2004) "This very interesting book is a great book teaching how to go from two-dimensional (2D)-images to three-dimensional (3D)-models of the geometry of a scene. … A good part of this book develops the foundations of an appropriate mathematical approach necessary for solving those difficult problems. … Exercises (drill exercises, advanced exercises and programming exercises) are provided at the end of each chapter." (Hans-Dietrich

包含所有可以找到的关于OpenCV 2 Computer Vision Application Programming Cookbook的资料。官方高清电子书，配套代码，实验所需要图片。免积分奉上，欢迎下载。opencv资源还很少，大家加油

对抗学习的半监督语义分割 此回购是以下论文的pytorch实现： ，，刘彦婷，， 英国机器视觉会议（BMVC）的会议记录，2018年。 联系人︰洪志智（whung8 at ucmerced dot edu） 该代码是从pytorch DeepLab实现（ ）大量借用的。 基线模型是DeepLabv2-Resnet101，没有进行多尺度培训和CRF后处理，在VOC2012验证集上的平均IOU为73.6% 。 如果发现对您的研究有用，请引用我们的论文。 @inproceedings{Hung_semiseg_2018, author = {W.-C. Hung and Y.-H. Tsai and Y.-T. Liou and Y.-Y. Lin and M.-H. Yang}, booktitle = {Proceedings of the British Machine

这是图像字幕的教程。 这是我正在编写的第一篇，是关于如何使用令人惊叹的PyTorch库自己实现酷模型的。 假定具备PyTorch，卷积和递归神经网络的基本知识。 如果您是PyTorch的新手，请先阅读PyTorch的和 。 问题，建议或更正可以作为问题发布。 我在Python 3.6使用PyTorch 0.4 。 2020年1月27日：添加了两个新教程的工作代码-和 内容 客观的 为了构建可以为图像生成描述性标题的模型，我们提供了它。 为了使事情简单，让我们实现“显示，出席和讲述”文件。 这绝不是当前的最新技术，但仍然相当惊人。 作者的原始实现可以在此处找到。 该模型学习在哪里看。 当您逐字生成标题时，可以看到模型的视线在图像上移动。 这是有可能的，因为它具有“注意”机制，该机制可以使其专注于图像中与下一个要说的单词最相关的部分。 以下是在训练或验证期间未看到的测试图