原理理解 1 使用3d gaussian representation对3d scene建模,这种建模方法效果好,训练效率高。 2 使用tile-based splatting solution来渲染,速度很快,对应submodules里的栅格化,是自己写的cuda代码,需要用nvcc编译 论文题目里两个术语分别对应建模和渲染两步 TODO b站有一个老师讲计算机图形学,对光栅化原理感兴趣的可以...
转换一下cmd里的项目地址 输入代码,从base 激活到 gaussian splatting conda activate gaussian_splatting base转换到gaussian splatting 文件夹返回到 gussian-splatting,输入2次 cd.. cd.. 2次cd.. 输入代码,把oldwall 换成你的文件夹名称 python convert.py -s data/oldwall 预训练,大概跑了5分钟,跑完继...
Open Windows Command Prompt by tying "cmd" into your search bar. Copy the below code into command prompt and press enter git clone https://github.com/graphdeco-inria/gaussian-splatting --recursive The folder will download to the root of our command line prompt with the name "Gaussian-Splat...
git clone https://github.com/graphdeco-inria/gaussian-splatting --recursive cd gaussian-splatting 然后在3dgspython环境中编译两个子模块: conda activate 3dgs pip install submodules/diff-gaussian-rasterization pip install submodules/simple-knn 然后就可以在Windows上正常训练与渲染了。 吐槽 作者在README...
windows系统里面的可视化界面是相对来说比较好安装的因为人家已经集成好了,直接下载到gaussian splatting的文件夹里解压就可以了。 10、设置启动脚本与data文件夹 (1)设置5个.bat脚本 在gaussian文件夹中先新建一个txt文档,然后分别将下面的指令对应放到文件中,记住最后要把.txt文件的后缀名改成.bat,如果想更改.bat...
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Windows: cd SIBR_viewers cmake -Bbuild . cmake --build build --target install --config RelWithDebInfo 你需要去下载一个软件,用于查看渲染结果,这里给出链接:https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/binaries/viewers.zip
Production-grade 3D gaussian splatting with CPU/GPU support for Windows, Mac and Linux 🚀 gaussian3dsplattingsplatsradiance-field UpdatedFeb 8, 2025 C++ PavelDoGreat/Super-Blur Star899 Code Issues Pull requests Screen and UI gaussian blur for Unity ...
WebGPU viewer for Gaussian Splatting nerfs This repository contains the source for an interactive web viewer of NeRFs crated with the code available fromINRIA. The app with instructions is hosted atjatentaki.github.io. Building This project has been created usingwebpack-cli. Before the first buil...
代码链接:https://github.com/nerfstudio-project/gsplat 2. 摘要 gsplat是一个开源库,用于训练和开发高斯分布方法。它的特点是前端具有与PyTorch库兼容的Python绑定,后端具有高度优化的CUDA内核。gsplat提供了许多增强高斯分布模型优化的特性,包括速度、内存和收敛时间的优化改进。实验结果表明,与原始实现相比,gsplat的...