Active View Selection with Perturbed Gaussian Ensemble for Tomographic Reconstruction

Sparse-view computed tomography (CT) is critical for re-ducing radiation exposure to patients. Recent advances in radiative 3DGaussian Splatting (3DGS) have enabled fast and accurate sparse-viewCT reconstruction. Despite these algorithmic advancements, practicalreconstruction fidelity remains fundamentally bounded by the qualityof the captured data, raising the crucial yet underexplored problem ofX-ray active view selection. Existing active view selection methods areprimarily designed for natural-light scenes and fail to capture the uniquegeometric ambiguities and physical attenuation properties inherent inX-ray imaging. In this paper, we present Perturbed Gaussian Ensemble,an active view selection framework that integrates uncertainty modelingwith sequential decision-making, tailored for X-ray Gaussian Splatting.Specifically, we identify low-density Gaussian primitives that are likely tobe uncertain and apply stochastic density scaling to construct an ensem-ble of plausible Gaussian density fields. For each candidate projection,we measure the structural variance of the ensemble predictions and selectthe one with the highest variance as the next best view. Extensive experi-mental results on arbitrary-trajectory CT benchmarks demonstrate thatour density-guided perturbation strategy effectively eliminates geometricartifacts and consistently outperforms existing baselines in progressivetomographic reconstruction under unified view selection protocols.