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gsplat: An Open-Source Library for Gaussian Splatting
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gsplat: An Open-Source Library for Gaussian Splatting
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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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Cited by 10 Pith papers
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GETA-3DGS: Automatic Joint Structured Pruning and Quantization for 3D Gaussian Splatting
GETA-3DGS is the first automatic joint structured pruning and quantization framework for 3D Gaussian Splatting, achieving roughly 5x storage reduction on standard datasets without per-scene thresholds.
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Single-Event Upsets in 3D Gaussian Splatting Rendering: Bit-Level Criticality, Spatial Extent, and a Parallel Support Guard
Bit flips in 3D Gaussian splatting are highly concentrated in effect with certain high-order bits corrupting up to 75.7% of the frame, but a support guard reduces the worst footprint to 11.68% while preserving clean p...
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Transcoding a 3D Gaussian Splatting Model from a Plenoptic Point Cloud or Mesh without the Original Multi-view Images
An end-to-end transcoding pipeline creates 3D Gaussian splatting models from plenoptic point clouds or meshes without original multi-view images, using custom initialization and surface constraints for high-quality ou...
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The Role of Initialization in 3D Gaussian Splatting
Dense initialization of 3DGS does not consistently beat sparse SfM initialization for standard novel views, but improves off-trajectory generalization; no densification method wins everywhere.
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Fast Wave-optics Rendering of Multiplane Images for 3D Holographic Displays
A new CGH pipeline converts multiplane-image stacks into random-phase holograms with wave-optics alpha compositing, matching Gaussian-based hologram quality while being orders of magnitude faster.
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The Role of Initialization in 3D Gaussian Splatting
Current densification methods in 3D Gaussian Splatting do not significantly benefit from dense initializations and perform similarly to sparse SfM-based ones.
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InstantSfM: Towards GPU-Native SfM for the Deep Learning Era
A fully GPU-native, PyTorch-based global Structure-from-Motion pipeline using sparse-aware Levenberg-Marquardt with optional metric depth priors reports ~8-40× speedups over COLMAP at comparable accuracy on several be...
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Global Motion Corresponder for 3D Point-Based Scene Interpolation under Large Motion
GMC learns per-point SE(3) mappings into a shared canonical space to interpolate and extrapolate 3D point-based scenes under large motion, outperforming baselines that assume small motion.
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3D-UIR: 3D Gaussian for Underwater 3D Scene Reconstruction via Physics Based Appearance-Medium Decoupling
Proposes a physics-based 3D Gaussian framework that disentangles appearance from medium effects for high-quality underwater novel view synthesis and scene restoration.
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Turbo-GS: Accelerating 3D Gaussian Fitting for High-Quality Radiance Fields
Turbo-GS accelerates 3D Gaussian Splatting training via dilated rendering of pixel subsets, convergence-aware Gaussian budget allocation, and combined positional-appearance error densification to enable faster 4K fitt...
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