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

arXiv:2609.30393v1 Announce Type: new 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(Mlog(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.

Related

• SA-ResGS: Self-Augmented Residual 3D Gaussian Splatting for Next Best View Selection
• FillGS: Filling Observation Gaps in 4D Gaussian Splatting via Viewpoint-Time Selection and Generative Refinement
• Active View Selection with Perturbed Gaussian Ensemble for Tomographic Reconstruction

Source: arXiv cs.CV | 2026-09-28