[Submitted on 24 Sep 2026]
Title:LiTe-GS: Oracle-Efficient Next Best View Selection for 3D Gaussian Splatting
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Abstract:Selecting informative camera views is critical for efficient training and adaptive refinement in 3D Gaussian Splatting, where each observation significantly influences model parameters. However, information-driven view-selection strategies can require repeated evaluations of expensive information-gain oracles as the number of candidate views increases. We propose LiTe-GS, an oracle-efficient method for next best view selection in 3D Gaussian Splatting. LiTe-GS reduces the number of information-oracle evaluations by performing randomized subset evaluation of candidate views rather than exhaustively scoring the full candidate pool. The resulting approach achieves expected $O(M\log(1/\epsilon))$ oracle complexity with respect to the number of candidate views $M$, independent of the selection cardinality $K$, while providing an explicit trade-off between oracle efficiency and approximation quality through $\epsilon$. We provide theoretical guarantees on oracle complexity and approximation performance under the proposed selection scheme. Experiments on Blender and Mip-NeRF 360 demonstrate that LiTe-GS maintains reconstruction quality comparable to Fisher-information-based baselines while substantially reducing the number of Fisher-oracle evaluations across different acquisition settings.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
Cite as: arXiv:2609.30393 [cs.CV]
(or arXiv:2609.30393v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.30393
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Vivek Pandey [view email] [v1] Thu, 24 Sep 2026 18:01:56 UTC (42,309 KB)
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