title: gsplat: An Open-Source Library for Gaussian Splatting
summary: 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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paper_authors.name: Jianbo Ye
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paper_authors.name: Matthew Tancik
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paper_title: gsplat: An Open-Source Library for Gaussian Splatting
paper_summary: 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.
paper_ai_summary: Gsplat is an open-source library for Gaussian Splatting with optimized CUDA kernels, offering speed, memory, and convergence improvements over the original implementation.
paper_ai_keywords: Gaussian Splatting PyTorch CUDA optimization convergence memory training time