c2a2o2-CSDN博客

转载中国计算机学会推荐国际学术会议

http://www.ccf.org.cn/xspj/rgzn/https://cmt3.research.microsoft.com/User/Login?ReturnUrl=%2FCVPR2019 prl是模式识别和计算机视觉邻域知名期刊，与之相当的期刊包括ivc，mva，paa，iet-ipr以及iet-cvi等，比之稍好的有tip，cviu以及pr等，顶级期刊是pami和ij...

2017-12-06 13:48:49 1051

转载基于深度学习的CVaaS计算机视觉即服务案例（Computer Vision as a Service）

技术与技法日进千里，快速迭代过程中，真正能够留下的是应用场景的重构与对新商业范式的思考。CVaaS 计算机视觉即服务的理念介绍观点来源于：极视角科技联合创始人罗韵CVaaS 就是 Computer Vision as a Service, 我们把 CV 的部分标准化成为了一种服务，而每一个行业可以在这里找到自己行业需要的和图像处理、视频处理、计算机视觉相关

2017-08-09 15:33:45 2151

原创 tw记账 Image Synthesis Image Inpainting MSI Afterburner pycorrector 聚类DBSCAN

sudo ./pycharm.sh嗨，如果您有64位系统，则需要对驱动程序进行签名。此驱动程序未签名，因此您必须通过点击F8 F8 F8重新启动，直到看到启动设置。选择禁用驱动程序签名强制执行，然后您可以安装驱动程序这解决了我的问题。问候约翰内斯self.woshow(self.real_A_seg_sng)def woshow(self,img): #img = t...

2017-04-28 19:21:37 1445 1

转载支持向量机通俗导论（理解SVM的三层境界）

前言动笔写这个支持向量机(support vector machine)是费了不少劲和困难的，原因很简单，一者这个东西本身就并不好懂，要深入学习和研究下去需花费不少时间和精力，二者这个东西也不好讲清楚，尽管网上已经有朋友写得不错了(见文末参考链接)，但在描述数学公式的时候还是显得不够。得益于同学白石的数学证明，我还是想尝试写一下，希望本文在兼顾通俗易懂的基础上，真真正正能足以成为一篇完

2017-04-16 09:47:27 706

In this paper, we present a method to optimize Gaussian splatting with a limited number of images while avoiding overfitting. Representing a 3D scene by combining numerous Gaussian splats has yielded outstanding visual quality. However, it tends to overfit

2024-04-23 12:21:21 452

原创 GaussianEditor：快速可控的3D编辑与高斯飞溅

在不断发展的计算机视觉领域，开发用户友好的3D表示和编辑算法是一个关键目标。这些技术在各种应用中至关重要，从数字游戏到不断增长的MetaVerse。传统的3D表示（如网格和点云）由于其交互式编辑功能而受到青睐。然而，这些方法在精确渲染复杂的3D场景方面面临挑战。

2024-04-23 10:53:29 355

原创基于高斯壳映射的高效三维人体生成

Efficient generation of 3D digital humans is important in several industries, including virtual reality, social media, and cinematic production. 3D generative adversarial networks (GANs) have demonstrated state-of-the-art (SOTA) quality and diversity for g

2024-04-23 10:37:36 299

原创负采样重要吗？它的理论与应用综述

Does Negative Sampling Matter? A Review with Insights into its Theory and Applications负采样重要吗？它的理论与应用综述Does Negative Sampling Matter? A Review with Insights into its Theory and ApplicationsZhen Yang, Ming Ding, Tinglin Huang, Yukuo Cen, Junshuai Song,

2024-04-21 16:46:21 806

原创 InFusion：通过从扩散先验学习深度完成来修复3D高斯

3D Gaussians have recently emerged as an efficient representation for novel view synthesis. This work studies its editability with a particular focus on the inpainting task, which aims to supplement an incomplete set of 3D Gaussians with additional points

2024-04-21 10:10:28 612

原创 Gamba：将高斯溅射与Mamba结合用于单视图3D重建

We tackle the challenge of efficiently reconstructing a 3D asset from a single image with growing demands for automated 3D content creation pipelines. Previous methods primarily rely on Score Distillation Sampling (SDS) and Neural Radiance Fields (NeRF). D

2024-04-21 09:58:38 613

原创三维高斯溅射的精确松弛约束

3D Gaussian splatting (3DGS) has recently demonstrated impressive capabilities in real-time novel view synthesis and 3D reconstruction. However, 3DGS heavily depends on the accurate initialization derived from Structure-from-Motion (SfM) methods. When trai

2024-04-21 09:52:07 653

原创 3D感知生成对抗网络的高斯溅射解码器

Gaussian Splatting Decoder for 3D-aware Generative Adversarial Networks3D感知生成对抗网络的高斯溅射解码器Florian Barthel1, 2 Arian Beckmann1 Wieland Morgenstern1 Anna Hilsmann1 Peter Eisert1,2Florian Barthel1, 2阿里安·贝克曼Wieland晨星Anna Hilsmann彼得·艾泽特1,21Fraunhofe

2024-04-21 09:43:31 610

原创 Spec-Gaussian：3D高斯溅射的各向异性视图相关外观

The recent advancements in 3D Gaussian splatting (3D-GS) have not only facilitated real-time rendering through modern GPU rasterization pipelines but have also attained state-of-the-art rendering quality. Nevertheless, despite its exceptional rendering qua

2024-04-21 08:44:43 552

原创解析溅射：通过解析积分进行抗混叠的3D高斯溅射

The 3D Gaussian Splatting (3DGS) gained its popularity recently by combining the advantages of both primitive-based and volumetric 3D representations, resulting in improved quality and efficiency for 3D scene rendering. However, 3DGS is not alias-free, and

2024-04-21 08:34:13 813

原创 GaussianCube：使用最优传输构造高斯溅射用于3D生成建模

3D Gaussian Splatting (GS) have achieved considerable improvement over Neural Radiance Fields in terms of 3D fitting fidelity and rendering speed. However, this unstructured representation with scattered Gaussians poses a significant challenge for generati

2024-04-21 08:25:48 516

原创 Recent Advances in 3D Gaussian Splatting

Recent Advances in 3D Gaussian Splatting，author = Tong Wu 1 ，Yu-Jie Yuan 1 ，Ling-Xiao Zhang 1 ，Jie Yang 1 ，Yan-Pei Cao 2 ，Ling-Qi Yan 3 ，and Lin Gao 1 \cor，runauthor = T.吴玉- J Yuan，L.-张旭，杨俊，杨毅- P Cao，L.- Q Yan，L.三维高斯溅射（3DGS）的出现大大加快了新视图合成的绘制速度。与神经辐射场（NeRF）等

2024-04-17 14:14:59 781

原创 PSAvatar：一种基于点的可变形形状模型，用于3D高斯溅射的实时头部化身创建

Despite much progress, achieving real-time high-fidelity head avatar animation is still difficult and existing methods have to trade-off between speed and quality. 3DMM based methods often fail to model non-facial structures such as eyeglasses and hairstyl

2024-04-16 10:47:16 494

原创用于密集视觉冲击的紧凑三维高斯散射Compact 3D Gaussian Splatting For Dense Visual SLAM

Recent work has shown that 3D Gaussian-based SLAM enables high-quality reconstruction, accurate pose estimation, and real-time rendering of scenes. However, these approaches are built on a tremendous number of redundant 3D Gaussian ellipsoids, leading to h

2024-04-16 10:35:18 383

原创 FreGS：具有渐进频率正则化的3D高斯溅射

3D Gaussian splatting has achieved very impressive performance in real-time novel view synthesis. However, it often suffers from over-reconstruction during Gaussian densification where high-variance image regions are covered by a few large Gaussians only,

2024-04-15 15:57:04 1072

原创 MVSplat：稀疏多视点图像的高效3D高斯溅射

We propose MVSplat, an efficient feed-forward 3D Gaussian Splatting model learned from sparse multi-view images. To accurately localize the Gaussian centers, we propose to build a cost volume representation via plane sweeping in the 3D space, where the cro

2024-04-15 15:49:12 791

原创 View-Consistent 3D Editing with Gaussian Splatting

The advent of 3D Gaussian Splatting (3DGS) has revolutionized 3D editing, offering efficient, high-fidelity rendering and enabling precise local manipulations. Currently, diffusion-based 2D editing models are harnessed to modify multi-view rendered images,

2024-04-12 15:24:58 428

原创 Sketch3D：用于草图到3D生成的样式一致性指南

Recently, image-to-3D approaches have achieved significant results with a natural image as input. However, it is not always possible to access these enriched color input samples in practical applications, where only sketches are available. Existing sketch-

2024-04-11 15:47:54 904

原创 GGRt: Towards Pose-free Generalizable 3D Gaussian Splatting in Real-time

This paper presents GGRt, a novel approach to generalizable novel view synthesis that alleviates the need for real camera poses, complexity in processing high-resolution images, and lengthy optimization processes, thus facilitating stronger applicability o

2024-04-11 15:37:30 1143

原创 StylizedGS: Controllable Stylization for 3D Gaussian Splatting

With the rapid development of XR, 3D generation and editing are becoming more and more important, among which, stylization is an important tool of 3D appearance editing. It can achieve consistent 3D artistic stylization given a single reference style image

2024-04-11 12:50:19 729

原创 Pixel-GS：用于3D高斯溅射的具有像素感知梯度的密度控制

3D Gaussian Splatting (3DGS) has demonstrated impressive novel view synthesis results while advancing real-time rendering performance. However, its efficacy heavily relies on the quality of the initial point cloud, leading to blurring and needle-like artif

2024-04-11 10:41:07 1019

原创 Deblurring 3D Gaussian Splatting去模糊3D高斯溅射

Recent studies in Radiance Fields have paved the robust way for novel view synthesis with their photorealistic rendering quality. Nevertheless, they usually employ neural networks and volumetric rendering, which are costly to train and impede their broad u

2024-04-11 10:34:41 781

原创 StyleNeRF：一个基于样式的3D感知生成器，用于高分辨率图像合成

StyleNeRF: A Style-based 3D-Aware Generator for High-resolution Image SynthesisStyleNeRF：一个基于样式的3D感知生成器，用于高分辨率图像合成Jiatao Gu†, Lingjie Liu‡, Peng Wang⋄, Christian Theobalt‡顾家涛 † ，刘玲杰 ‡ ，王鹏 ⋄ ，克里斯蒂安·西奥博尔特 ‡†Facebook AI ‡Max Planck Institute for Info

2024-04-11 09:38:17 902

原创 ImplicitDeepfake：通过使用NeRF和高斯溅射的隐式Deepfake生成的合理换脸

相比之下，GS通过将对象的特征编码在高斯分布的集合中，提供了加速的训练和推理，而不会降低渲染质量。deepfake的示例方法由生成高保真单次转移（GHOST）（Groshev等人，2022），其中作者建立在FaceShifter（Li等人，2019）模型作为起点，并介绍了deepfake的质量和稳定性的几个增强功能。为了获得真实的单词3D对象，我们使用新颖的、最先进的基于机器学习的方法，例如神经辐射场（NeRFs）（Mildenhall等人，2020）和高斯溅射（GS）（Kerbl等人，2023年）。

2024-04-11 09:10:05 808

原创 3D-Aware Multi-Class Image-to-Image Translation with NeRFs

Recent advances in 3D-aware generative models (3D-aware GANs) combined with Neural Radiance Fields (NeRF) have achieved impressive results. However no prior works investigate 3D-aware GANs for 3D consistent multi-class image-to-image (3D-aware I2I) transla

2024-04-10 17:37:42 518

转载一文带你入门 3D Gaussian Splatting

3D 高斯的协方差矩阵 Σ� 表示了其在空间中的形状和方向，它可以用一个对称正定矩阵来表示，也可以用一个对角矩阵和一个旋转矩阵的乘积来表示，以减少参数的数量。3D 高斯的权重 �� 表示了其在场景中的贡献，它可以用一个非负的标量来表示，也可以用一个球谐函数来表示，以增加其与视图相关的表达能力。很形象，我们可以想象一下，把一个雪球（高斯球）扔到一个玻璃盘子上，雪球散开以后，在撞击中心的雪量（对图像的贡献）最大，而随着离撞击中心距离的增加，雪量（贡献）减少。在整个空间中，每个高斯球又是离散的。

2024-03-01 10:15:54 1482

转载 MWCNN中使用的haar小波变换 pytorch

用图像表述如图所示：图中a表示原图，图b表示经过一级小波变换的结果，h1 表示水平反向的细节，v1 表示竖直方向的细节，c1表示对角线方向的细节，b表示下2采样的图像。B）高频采用差均值，即b[2]和b[3]，用于记录图像的细节信息，这样在重构时能够恢复图像的全部信息。在这里的行x1=x_LL/2, x2=x_HL/2, x3=x_LH/2, x4=x_HH/2。A）低频采用和均值，即b[0]和b[1]，和均值中均值存储了图像的整体信息。# 使用哈尔 haar 小波变换来实现二维逆向离散小波。

2023-11-27 15:27:37 861

转载可逆神经网络的研究及其在图像中应用

一、摘要可逆神经网络(INN)自被提出以来，就受到了广泛关注。由于其双射构造和高效可逆性，INN被用于各种推理任务，如图像隐藏、图像重缩放、图像着色、图像压缩和视频超分辨率等等。本文针对最新关于INN在图像方面应用的文献进行介绍，包括每篇文献的基本原理和个人理解。最后对所介绍的文献进行总结，指出各自的优缺点并对未来INN在图像方面应用展开思考。Invertible neural network (INN) has been widely concerned since it was proposed. Be

2023-11-10 10:33:41 400

转载 Robust Invertible Image Steganography

在正向转换过程中，CANP可以将输入的图像对[ x s ， x h ] [x_s，x_h][x。一同输入backward块中，得到了恢复的秘密和host图像[ X s ′ , X h ′ ] [X_s',X_h'][X。(为了输入方便，这里用y’代替图中y~)图像，通过特征增强模块以消除噪声和jpeg压缩的影响，随后从y ′ y'y。，双射RIIS可以在反向传递中生成[ X s ′ , X h ′ ] [X_s',X_h'][X。基于流的可逆块将输入的[ X s , X h ] [X_s,X_h][X。

2023-11-01 14:11:00 457

转载跑通代码-ICCV2021-HiNet: Deep Image Hiding by Invertible Network

第一排是原始的宿主图像，第二排是原始的水印图像，也就是需要嵌入到原始宿主图像的信息，第三排是嵌入水印图像之后的图像，能看到，嵌入水印之后的图像与原始的图像非常的相似，肉眼几乎无法分辨，说明算法的不可感知性很好，最后一排是提取出来的水印图像，与第二排的原始水印图像很相近，说明算法的鲁棒性很好，恢复水印图像的能力很强。在图像水印算法中，应该对嵌入水印图像之后的图像进行图像攻击，然后再提取水印图像，如果依然能很好的提取出来水印图新，说明算法的鲁棒性很好，这里的攻击可以是噪声攻击，滤波攻击，几何攻击等等。

2023-10-24 10:36:41 1017 5

ffmpeg-4.2.1-win64-win10-然后pip就好

dlib安装 dlib-19.19.0-cp38-cp38-win_amd64.whl.whl

dlib-19.17.99-cp37-cp37m-win_amd64.whl

coreseek-4

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