{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/10"}],"enrichment":{"capability":"gsplat is a CUDA-accelerated Python library for rasterizing 3D Gaussians, implementing differentiable rendering based on 3D Gaussian Splatting with support for multiple camera models and optimization strategies.","skillfed_tags":["3d-graphics","neural-rendering","cuda-acceleration"],"use_cases":["Train 3D Gaussian splatting models on COLMAP or NCore captures for real-time novel-view synthesis.","Reconstruct large-scale 3D scenes with sparse rendering and multi-GPU training for memory efficiency.","Render pre-trained Gaussian scenes at low latency using the experimental HiGS inference path.","Optimize 3D Gaussian parameters using MCMC or standard densification strategies for scene fitting.","Fit 2D images or 3D point clouds with differentiable Gaussian rasterization for inverse graphics tasks."],"what_it_does":"gsplat is a Python library that implements fast, memory-efficient CUDA-accelerated rasterization of 3D Gaussians for neural rendering. It builds on the 3D Gaussian Splatting paper but optimizes the core rendering kernels and adds support for multiple camera models (pinhole, fisheye, LiDAR), sparse rendering, multi-GPU distributed training, and alternative optimization strategies like MCMC. The library is designed for training and rendering neural radiance fields and 3D scene reconstructions, with both dense and sparse rendering paths.\n\nThe package depends on PyTorch for tensor operations, numpy for numerical work, torch's ninja build system, and several utility libraries (jaxtyping, rich, typing_extensions). It requires a CUDA-capable GPU and compiles CUDA kernels either during installation or on first run. Recent updates (as of mid-2026) include spherical harmonics improvements, orthographic camera support, LiDAR rendering, and experimental low-latency inference rendering via HiGS.","worth_installing":"Yes, if you have a CUDA GPU and need fast, differentiable 3D Gaussian rendering. The library is actively maintained, has low install friction, and offers significant performance and memory improvements over the reference implementation. However, verify the license terms in the repository first, and confirm your PyTorch and CUDA versions are supported by pre-compiled wheels or compatible with JIT compilation."},"id":"gsplat","links":{"html":"https://skillfed.io/packages/gsplat","md":"https://skillfed.io/packages/gsplat.md","pypi":"https://pypi.org/project/gsplat/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-07-04","license_spdx":null,"license_treatment":"unclear","name":"gsplat","python_support":"supports_current","summary":"Python package for differentiable rasterization of gaussians"},"popularity":{"monthly_downloads":99914,"position":13009,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.5.3"}