开源工具gsplat显著加速并降低高斯点渲染的内存消耗
gsplat: An Open-Source Library for Gaussian Splatting
- 基于PyTorch与CUDA优化，实现高效训练
- 相比原版快10%、内存降低4倍
- 适合研究与开发高斯点渲染的开发者
gsplat 是一个开源库，专为训练和开发高斯点渲染（Gaussian Splatting）方法设计。其前端支持与 PyTorch 兼容的 Python 接口，后端采用高度优化的 CUDA 内核。该库提供多项优化功能，显著提升模型训练的速度、内存占用和收敛效率。实验结果表明，gsplat 在训练时间上最多可减少 10%，内存使用量降低至原实现的 1/4。该工具已被多个研究项目采用，并在 GitHub 上持续维护。源代码公开于 https://github.com/nerfstudio-project/gsplat，采用 Apache License 2.0 许可协议，欢迎社区贡献。
原文摘要 · Abstract (English)
gsplat is an open-source library designed for training and developing Gaussian Splatting methods. It features a front-end with Python bindings compatible with the PyTorch library and a back-end with highly optimized CUDA kernels. gsplat offers numerous features that enhance the optimization of Gaussian Splatting models, which include optimization improvements for speed, memory, and convergence times. Experimental results demonstrate that gsplat achieves up to 10% less training time and 4x less memory than the original implementation. Utilized in several research projects, gsplat is actively maintained on GitHub. Source code is available at https://github.com/nerfstudio-project/gsplat under Apache License 2.0. We welcome contributions from the open-source community.
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