{"id":15440,"date":"2025-04-18T21:57:42","date_gmt":"2025-04-18T13:57:42","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/15440.html"},"modified":"2025-04-18T21:57:42","modified_gmt":"2025-04-18T13:57:42","slug":"3dgs%e5%9c%a8%e6%9c%8d%e5%8a%a1%e5%99%a8%e4%b8%8a%e8%bf%90%e8%a1%8c%e7%9a%84%e7%8e%af%e5%a2%83%e6%90%ad%e5%bb%ba","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/15440.html","title":{"rendered":"3DGS\u5728\u670d\u52a1\u5668\u4e0a\u8fd0\u884c\u7684\u73af\u5883\u642d\u5efa"},"content":{"rendered":"<p>\u5728\u5b66\u6821\u670d\u52a1\u5668\u4e0a\u60a0\u95f2\u5730\u642d\u4e86\u4e24\u661f\u671f\u73af\u5883\u90fd\u6ca1\u642d\u8d77\u6765&#xff08;\u8ddf\u6ca1\u6709sudo\u6743\u9650\u548c\u6211\u6709\u9006\u53cd\u5fc3\u7406\u6709\u5173&#xff09;&#xff0c;\u4e0a\u7f51\u79df\u4e86\u4e00\u4e2a\u670d\u52a1\u5668\u4e00\u5929\u641e\u5b8c&#xff08;\u4e5f\u6709\u667a\u529b\u5927\u7206\u53d1\u7684\u6210\u5206\u5728&#xff09;<\/p>\n<p>\u53c2\u8003&#xff1a;<\/p>\n<p>1.B\u7ad9\u4e0a\u7684\u89c6\u9891\u8bb2\u89e3&#xff0c;UP&#xff1a;heawon-yoon<\/p>\n<p>https:\/\/github.com\/heawon-yoon\/smpl_gaussian_tutorial<\/p>\n<p>2.CSDN\u4e0a\u7684\u5e2e\u52a9<\/p>\n<p>https:\/\/blog.csdn.net\/sichen1997\/article\/details\/138169731<\/p>\n<p>\u6211\u7528\u7684\u662fAUTODL\u7684\u670d\u52a1\u5668<\/p>\n<p>\u955c\u50cf\u9009\u7684\u662fminiconda3\u3001Python3.10&#xff08;Ubantu22.04&#xff09;\u3001CUDA11.8<\/p>\n<p>\u79df\u597d\u6253\u5f00&#xff0c;\u6211\u662f\u76f4\u63a5\u4f7f\u7528\u7f51\u9875\u7248\u7684JupyterLab&#xff08;\u4f46\u662f\u975e\u5e38\u70e6\u4eba&#xff0c;\u4e00\u6302\u68af\u5b50\u5c31\u65ad\u7ebf&#xff0c;\u800c\u4e14\u6821\u56ed\u7f51\u6162\u5f97\u8981\u6b7b&#xff0c;\u6bcf\u6b21\u4eceGitHub\u4e0a\u9762\u4e0b\u8f7d\u4ee3\u7801\u90fd\u767b\u4e0d\u4e0a\u53bb&#xff0c;\u8fd8\u8981\u6211\u7ed9\u5b83\u5f00\u70ed\u70b9&#xff0c;\u5efa\u8bae\u6709\u65f6\u95f4\u7684\u540c\u5b66\u7528IDE\u4e0a\u7684\u63d2\u4ef6&#xff09;<\/p>\n<p>\u9996\u5148\u521d\u59cb\u5316\u4e00\u4e0bconda\u73af\u5883<\/p>\n<p>conda init <\/p>\n<p>\u7136\u540e\u91cd\u65b0\u6253\u5f00\u7ec8\u7aef&#xff0c;\u770b\u5230\u524d\u9762\u6709&#xff08;base&#xff09;\u5c31\u8bf4\u660e\u597d\u4e86<\/p>\n<p>\u63a5\u4e0b\u6765\u4eceGitHub\u4e0a\u9762\u628a\u4ee3\u7801\u641e\u4e0b\u6765<\/p>\n<p>git clone https:\/\/github.com\/graphdeco-inria\/gaussian-splatting &#8211;recursive<br \/>\ncd gaussian-splatting <\/p>\n<p>\u8fd9\u4e00\u6b65\u7279\u522b\u8981\u6ce8\u610f&#xff0c;\u4e0d\u80fd\u4ece\u5b98\u7f51\u4e0a\u4e0b\u8f7dzip\u538b\u7f29\u5305\u7136\u540e\u518d\u76f4\u63a5\u4f20\u5230JupyterLab\u91cc\u9762&#xff0c;\u56e0\u4e3a\u5982\u679c\u76f4\u63a5\u4f20&#xff0c;\u4f1a\u5bfc\u81f4submodules\u91cc\u9762\u7684\u5305\u4e0d\u80fd\u4ee5Git\u4ed3\u7684\u5f62\u5f0f\u5b89\u88c5&#xff08;\u597d\u50cf\u662f\u56e0\u4e3a\u6ca1\u6709\u5efa\u7acb\u94fe\u63a5&#xff1f;&#xff09;<\/p>\n<p>\u7136\u540e\u521b\u5efa\u865a\u62df\u73af\u5883<\/p>\n<p>conda create -n gaussian python&#061;3.10 -y<br \/>\nconda activate gaussian <\/p>\n<p>\u63a5\u4e0b\u6765\u5b89\u88c5torch&#xff08;\u8fd9\u4e2a\u662f\u6700\u70e6\u7684&#xff0c;\u6211\u7684\u5927\u90e8\u5206\u9519\u8bef\u90fd\u51fa\u5728\u8fd9\u4e9b\u4f9d\u8d56\u4e0d\u517c\u5bb9\u7684\u95ee\u9898\u4e0a&#xff0c;\u6240\u4ee5\u4e00\u5b9a\u8981\u770b\u597d\u7248\u672c\u76f8\u4e92\u4e4b\u95f4\u662f\u5426\u517c\u5bb9&#xff09;&#xff0c;\u8fd9\u4e2a\u662f\u6211\u7528\u7684&#xff0c;\u5b9e\u6d4b\u8fd8\u662f\u53ef\u4ee5\u7684\u3002<\/p>\n<p>conda install pytorch&#061;&#061;2.0.1 torchvision&#061;&#061;0.15.2 torchaudio&#061;&#061;2.0.2 pytorch-cuda&#061;11.8 -c pytorch -c nvidia<\/p>\n<p>\u00a0\u8fd9\u4e9b\u90fd\u6ca1\u95ee\u9898\u4e86\u5c31\u53ef\u4ee5\u4e0b\u4e00\u6b65&#xff0c;\u628aC&#043;&#043;\u76f8\u5173\u7684\u90fd\u88c5\u4e0a<\/p>\n<p>sudo apt-get update<br \/>\nsudo apt install build-essential<br \/>\nsudo apt-get install ninja-build <\/p>\n<p>\u7136\u540e\u6709\u7684\u670b\u53cb\u4f1a\u53d1\u73b0\u5b89\u4e86\u8fd9\u4e9b\u76f8\u540c\u7684\u8fd8\u662f\u5728\u88c5submodules\u7684\u65f6\u5019\u51fa\u9519&#xff0c;\u8fd9\u65f6\u5019\u8981\u68c0\u67e5\u4e00\u4e0b\u4f60\u7cfb\u7edf\u7684\u5176\u4ed6\u8ddfC&#043;&#043;\u76f8\u5173\u7684\u4f9d\u8d56&#xff1a;<\/p>\n<p>nvidia -smi<br \/>\nnvcc &#8211;version<br \/>\ngcc &#8211;version<br \/>\ng&#043;&#043; &#8211;version<br \/>\nmake &#8211;version<br \/>\ncmake &#8211;version <\/p>\n<p>\u8fd9\u4e9b\u547d\u4ee4\u8981\u5f97\u5230&#xff1a;\u670d\u52a1\u5668\u5b89\u88c5\u4e86CUDA&#xff08;\u8fd9\u4e2a\u4e00\u822c\u90fd\u6709&#xff0c;\u5e76\u4e14\u662fCUDA12\u5f80\u4e0a&#xff09;\u3001nvcc\u7248\u672c\u8981\u662f11.8&#xff0c;gcc\u8981\u5c0f\u4e8e12&#xff08;\u4e5f\u5c31\u662f11\u70b9\u51e0\u90fd\u53ef\u4ee5&#xff0c;\u6211\u770b\u5230\u6709\u670b\u53cb\u8bf49.4\u4e5f\u53ef\u4ee5&#xff0c;\u4e0d\u8fc7\u6211\u6ca1\u6709\u8bd5&#xff09;\u3001g&#043;&#043;\u8981\u4e0egcc\u540c\u7248\u672c&#xff08;\u4e00\u822c\u90fd\u662f\u540c\u7248\u672c&#xff09;\u3001make&#xff08;\u8fd9\u4e2a\u6211\u5fd8\u8bb0\u4e86&#xff0c;\u4f46\u662f\u5e94\u8be5\u4e0d\u5927\u5f71\u54cd&#xff09;\u3001cmake3.22.1&#xff08;\u6211\u770b\u5230\u6709\u670b\u53cb\u75283.23.0\u4e5f\u53ef\u4ee5&#xff0c;\u6211\u7684\u8fd9\u4e2a3.22.1\u662f\u53ef\u4ee5\u517c\u5bb9\u7684&#xff09;<\/p>\n<p>\u7136\u540e\u5c31\u53ef\u4ee5\u7f8e\u7f8e\u88c5\u5b50\u6a21\u5757\u4e86<\/p>\n<p>pip install submodules\/diff-gaussian-rasterization<br \/>\npip install submodules\/simple-knn <\/p>\n<p>\u8fd9\u4e24\u4e2a\u90fd\u88c5\u4e0a&#xff0c;\u4f60\u5c31\u57fa\u672c\u4e0a\u5df2\u7ecf\u6210\u529f\u4e86<\/p>\n<p>\u7136\u540e\u628a\u9700\u8981\u7684\u4f9d\u8d56\u90fd\u88c5\u4e0a&#xff0c;\u57fa\u672c\u4e0a\u5c31\u662f\u7f3a\u5565\u8865\u5565&#xff0c;\u975e\u5e38\u5feb<\/p>\n<p>pip install plyfile tensorboard tqdm six numpy&#061;&#061;1.22<br \/>\npip install opencv-python<br \/>\npip install opencv-contrib-python<\/p>\n<p>\u7136\u540e\u4e0a\u7f51\u7ad9\u4e0a\u4e0b\u8f7d\u6570\u636e\u96c6&#xff0c;\u5728\u9879\u76ee\u6587\u4ef6\u5939\u91cc\u5efa\u7acb\u4e00\u4e2a\u65b0\u6587\u4ef6\u5939data&#xff0c;\u628a\u60f3\u8dd1\u7684\u6570\u636e\u632a\u8fdb\u53bb<\/p>\n<p>\u8bad\u7ec3&#xff01;&#xff08;\u8fd9\u91cc\u7528\u7684\u662f\u5361\u8f66\u6570\u636e\u96c6&#xff09;<\/p>\n<p>python train.py -s .\/data\/tandt\/truck <\/p>\n<p>\u7136\u540e\u5b9e\u65f6\u67e5\u770b&#xff1a;\u91cd\u65b0\u6253\u5f00\u4e00\u4e2a\u65b0\u7684\u7ec8\u7aef\u7a97\u53e3<\/p>\n<p>\u8fdb\u5165\u81ea\u5df1\u7684\u8fd9\u4e2a\u865a\u62df\u73af\u5883&#xff0c;\u6253\u5f00tensorboard<\/p>\n<p>tensorboard &#8211;logdir&#061;.\/output\/XXXX <\/p>\n<p>\u8fd9\u4e2aoutput\u540e\u9762\u7684\u5185\u5bb9\u89c6\u60c5\u51b5\u66f4\u6539&#xff0c;\u5c31\u662f\u5728train\u7684\u90a3\u4e00\u6761\u4ee3\u7801\u7684\u7ed3\u679c\u91cc\u7684output folder<\/p>\n<p>\u7136\u540e\u628a\u81ea\u5df1\u7535\u8111Windows\u7684cmd\u6253\u5f00&#xff0c;\u8f93\u5165\u670d\u52a1\u5668\u6240\u7ed9\u7684ssh\u3002\u3002\u3002\u7684\u4e00\u6bb5\u547d\u4ee4&#xff0c;\u5c31\u53ef\u4ee5\u6253\u5f00\u547d\u4ee4\u6240\u7ed9\u7684\u94fe\u63a5localhost 6006\u4e86&#xff0c;\u8fd9\u65f6\u5019\u5c31\u80fd\u67e5\u770b\u8bad\u7ec3\u7ed3\u679c\u4e86&#xff01;<\/p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u6587\u7ae0\u6d4f\u89c8\u9605\u8bfb987\u6b21\uff0c\u70b9\u8d5e10\u6b21\uff0c\u6536\u85cf12\u6b21\u3002\u8fd9\u4e9b\u547d\u4ee4\u8981\u5f97\u5230\uff1a\u670d\u52a1\u5668\u5b89\u88c5\u4e86CUDA\uff08\u8fd9\u4e2a\u4e00\u822c\u90fd\u6709\uff0c\u5e76\u4e14\u662fCUDA12\u5f80\u4e0a\uff09\u3001nvcc\u7248\u672c\u8981\u662f11.8\uff0cgcc\u8981\u5c0f\u4e8e12\uff08\u4e5f\u5c31\u662f11\u70b9\u51e0\u90fd\u53ef\u4ee5\uff0c\u6211\u770b\u5230\u6709\u670b\u53cb\u8bf49.4\u4e5f\u53ef\u4ee5\uff0c\u4e0d\u8fc7\u6211\u6ca1\u6709\u8bd5\uff09\u3001g++\u8981\u4e0egcc\u540c\u7248\u672c\uff08\u4e00\u822c\u90fd\u662f\u540c\u7248\u672c\uff09\u3001make\uff08\u8fd9\u4e2a\u6211\u5fd8\u8bb0\u4e86\uff0c\u4f46\u662f\u5e94\u8be5\u4e0d\u5927\u5f71\u54cd\uff09\u3001cmake3.22.1\uff08\u6211\u770b\u5230\u6709\u670b\u53cb\u75283.23.0\u4e5f\u53ef\u4ee5\uff0c\u6211\u7684\u8fd9\u4e2a3.22.1\u662f\u53ef\u4ee5\u517c\u5bb9\u7684\uff09\u7684\u4e00\u6bb5\u547d\u4ee4\uff0c\u5c31\u53ef\u4ee5\u6253\u5f00\u547d\u4ee4\u6240\u7ed9\u7684\u94fe\u63a5localhost 6006\u4e86\uff0c\u8fd9\u65f6\u5019\u5c31\u80fd\u67e5\u770b\u8bad\u7ec3\u7ed3\u679c\u4e86\uff01\u8fd9\u4e9b\u90fd\u6ca1\u95ee\u9898\u4e86\u5c31\u53ef\u4ee5\u4e0b\u4e00\u6b65\uff0c\u628aC++\u76f8\u5173\u7684\u90fd\u88c5\u4e0a\u3002_3dgs tensorboard<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[43,44],"topic":[],"class_list":["post-15440","post","type-post","status-publish","format-standard","hentry","category-server","tag-43","tag-44"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>3DGS\u5728\u670d\u52a1\u5668\u4e0a\u8fd0\u884c\u7684\u73af\u5883\u642d\u5efa - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link 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content=\"\u6587\u7ae0\u6d4f\u89c8\u9605\u8bfb987\u6b21\uff0c\u70b9\u8d5e10\u6b21\uff0c\u6536\u85cf12\u6b21\u3002\u8fd9\u4e9b\u547d\u4ee4\u8981\u5f97\u5230\uff1a\u670d\u52a1\u5668\u5b89\u88c5\u4e86CUDA\uff08\u8fd9\u4e2a\u4e00\u822c\u90fd\u6709\uff0c\u5e76\u4e14\u662fCUDA12\u5f80\u4e0a\uff09\u3001nvcc\u7248\u672c\u8981\u662f11.8\uff0cgcc\u8981\u5c0f\u4e8e12\uff08\u4e5f\u5c31\u662f11\u70b9\u51e0\u90fd\u53ef\u4ee5\uff0c\u6211\u770b\u5230\u6709\u670b\u53cb\u8bf49.4\u4e5f\u53ef\u4ee5\uff0c\u4e0d\u8fc7\u6211\u6ca1\u6709\u8bd5\uff09\u3001g++\u8981\u4e0egcc\u540c\u7248\u672c\uff08\u4e00\u822c\u90fd\u662f\u540c\u7248\u672c\uff09\u3001make\uff08\u8fd9\u4e2a\u6211\u5fd8\u8bb0\u4e86\uff0c\u4f46\u662f\u5e94\u8be5\u4e0d\u5927\u5f71\u54cd\uff09\u3001cmake3.22.1\uff08\u6211\u770b\u5230\u6709\u670b\u53cb\u75283.23.0\u4e5f\u53ef\u4ee5\uff0c\u6211\u7684\u8fd9\u4e2a3.22.1\u662f\u53ef\u4ee5\u517c\u5bb9\u7684\uff09\u7684\u4e00\u6bb5\u547d\u4ee4\uff0c\u5c31\u53ef\u4ee5\u6253\u5f00\u547d\u4ee4\u6240\u7ed9\u7684\u94fe\u63a5localhost 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