环境为windows10下的WSL2 Ubuntu 20.04.6 LTS, 我在此环境下配置高斯相关环境时, 安装submodules时报错如上, 此时我在Ubuntu终端下运行nvidia-smi是可以看到CUDA Version: 12.6 的, 运行import torch print(torch.cuda.is_available()) 也是弹出TRUE, 这就很奇怪了, 而且此时/usr/local下是没有cuda的相关文件的...
使用pip命令安装diff-gaussian-rasterization库: 打开你的命令行工具(如终端或命令提示符),然后运行以下命令来安装diff-gaussian-rasterization: bash pip install diff-gaussian-rasterization 如果你使用的是特定版本的Python(例如Python 3.x),并且系统中同时安装了Python 2.x,你可能需要使用pip3来确保为正确的Python版...
add depth rendering implemented in depth-diff-gaussian-rasterization. add importance score for each 3D gaussian based on its contribution to rendering. add opacity rendering in forward pass. add n_touched to count the number of pixels each Gaussian contributes to Differential Gaussian Rasterization Used...
Note: this is the original readme for the original diff-gaussian-rasterization repository.Used as the rasterization engine for the paper "3D Gaussian Splatting for Real-Time Rendering of Radiance Fields". If you can make use of it in your own research, please be so kind to cite us....
diff-gaussian-rasterization(3DGDStream)是一个CUDA实现的模块,用于3D Gaussian splatting中的光栅化核心算法。这个模块提供了forward和backward两个函数,分别用于前向传播和后向传播。它通过计算协方差矩阵梯度、缩放和旋转参数的梯度,以及2D椭圆二次型矩阵的梯度,实现了3D Gaussian splatting的核心算法。该模块还包含了...
上期写了python代码大致解读,这期写一下cuda源码部分,主要涉及到使用的diff_gaussian_rasterization模块。主要的代码如下: submodules/diff-gaussian-rasterization ├── CMakeLists.txt ├── cuda_rasterizer │ ├── auxiliary.h │ ├── backward.cu │ ├── backward.h │ ├── config.h │ ├─...
使用IDEA 插件离线检测 将OpenSCA 扫描能力集成到 IntelliJ 平台 IDE 工具,随时随地保障组件依赖安全。如何使用 了解详情 使用OpenSCA CLI 扫描分析 OpenSCA CLI 是一款开源的软件成分分析工具,用来扫描项目的第三方开源组件依赖及漏洞信息。如何使用 了解详情
GaussionTalker 中 diff_gaussian_rasterization test
⚠️ NOTE: The input to rasterization are slightly different. It is recommended to create a new environment to avoid conflict. Install our modified Rasterization: # Install our modified code (cuda) git clone git@github.com:npu-yanchi/diff-gaussian-rasterization-for-gsslam.git cd diff-gaussian...
Processing f:\ai\gs\gaussian-splatting\submodules\diff-gaussian-rasterization Preparing metadata (setup.py): started Preparing metadata (setup.py): finished with status 'done' Processing f:\ai\gs\gaussian-splatting\submodules\simple-knn Preparing metadata (setup.py): started ...