Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Gsplat: An Open-Source Library for Gaussian Splatting. |
| Συγγραφείς: |
Ye, Vickie VYE@BERKELEY.EDU, Li, Ruilong RUILONGLI@BERKELEY.EDU, Kerr, Justin JUSTINKERR@BERKELEY.EDU, Turkulainen, Matias1 MATIAS.TURKULAINEN@AALTO.FI, Yi, Brent BRENTYI@BERKELEY.EDU, Pan, Zhuoyang2 PANZHY@SHANGHAITECH.EDU.CN, Seiskari, Otto OTTO.SEISKARI@SPECTACULARAI.COM, Ye, Jianbo JIANBOYE.AI@GMAIL.COM, Hu, Jeffrey HUJH14@GMAIL.COM, Tancik, Matthew MATT@LUMALABS.AI, Kanazawa, Angjoo KANAZAWA@EECS.BERKELEY.EDU |
| Πηγή: |
Journal of Machine Learning Research. Jan-Dec2025, Vol. 26, p1-17. 17p. |
| Θεματικοί όροι: |
*Software libraries (Computer programming), *Parallel programming, *Mathematical optimization, Python programming language, Computer memory management, Three-dimensional imaging |
| Περίληψη: |
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 Py-Torch 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 4 less memory than the original Kerbl et al. (2023) 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. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: |
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