LiTe-GS: Oracle-Efficient Next Best View Selection for 3D Gaussian Splatting

arXiv:2609.30393 · cs.CV, cs.RO · Submitted 2026-09-24 · Read on arXiv

cs.CV, cs.RO

Submitted: 2026-09-24

Updated: 2026-09-24

License: http://creativecommons.org/licenses/by/4.0/

The gist: Selecting informative camera views is critical for efficient training and adaptive refinement in 3D Gaussian Splatting, where each observation significantly influences model parameters.

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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 (1/ε)) 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 ε. 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.

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