[2605.06876] AdpSplit: Error-Driven Adaptive Splitting for Faster Geometry Discovery in 3D Gaussian Splatting
[Submitted on 7 May 2026]
Title:AdpSplit: Error-Driven Adaptive Splitting for Faster Geometry Discovery in 3D Gaussian Splatting
View a PDF of the paper titled AdpSplit: Error-Driven Adaptive Splitting for Faster Geometry Discovery in 3D Gaussian Splatting, by Yongjae Lee and 4 other authors
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Abstract:Adaptive density control in 3D Gaussian Splatting (3DGS) repeatedly grows the Gaussian population through fixed-cardinality random splitting to discover useful scene structure. However, in vanilla 3DGS, its binary split operator requires many densification rounds to expose fine details, making it a bottleneck for efficient training schedules with fewer iterations. We introduce AdpSplit, an error-driven adaptive split operator that determines the number of split children and initializes the child parameters from L1-pixel-error region statistics, enabling fewer densification iterations, thus reduced training time, while preserving the rendering quality of full-schedule training. Across the MipNeRF360, Deep-Blending, and Tanks&Temples datasets, AdpSplit reduces the training time of multiple accelerated 3DGS pipelines by 9.2%-22.3% as a simple drop-in replacement for the standard split operator. With FastGS, AdpSplit matches the full-schedule PSNR on MipNeRF360 while reducing training time by 16.4%, corresponding to a 12.6x acceleration over vanilla 3DGS.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.06876 [cs.CV]
(or arXiv:2605.06876v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.06876
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yongjae Lee [view email] [v1] Thu, 7 May 2026 19:23:16 UTC (17,389 KB)
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