DC-DLO: Decoupling Local Repair and Pose Discovery for 3D Gaussian Splatting

3D Gaussian Splatting (3DGS) has shown strong performance in novel-view synthesis, but few-view reconstruction remains highly sensitive to which observations are acquired under a limited budget. Active view selection offers a natural way to address this sensitivity by sequentially choosing additional views. However, applying sequential selection to 3DGS is complicated by its evolving representation, as optimization, densification, and pruning continually change the underlying Gaussian primitives. As a result, selection states indexed by the current primitives lack a stable spatial reference across acquisition rounds. To address this issue, we propose DC-DLO, a decoupled active view selection framework that separates model-based local repair from pose-only discovery. We first introduce World-Space Local Repair (WSLR), which rebuilds local observability on a fixed world-space voxel grid at every round to avoid dependence on persistent Gaussian identities. Within each occupied cell, a compact \(3\times3\) matrix encodes directional support from the acquired cameras. Candidate views are evaluated by the marginal log-determinant gain of these local matrices, and an adaptive Top-K rule aggregates the largest local gains to form the repair score. Then, to preserve pose diversity independently of current model support, we introduce Pose Discovery, a pose-only gate that constrains the feasible candidate set without modifying the repair score. Candidate RGB is never accessed before selection and is revealed only after a view is chosen. We evaluate DC-DLO on the full nine-scene Mip-NeRF 360 benchmark, the seven-scene Extended NBV benchmark, and the four-scene Light-Field benchmark. When results are aggregated over scenes within each benchmark, DC-DLO achieves the highest PSNR and SSIM and the lowest LPIPS among the evaluated selectors on all three benchmarks. Matched ablations further support the benefit of rebuilding the local scoring state in world space.