Group-Wise Simplification of Radiative Gaussian Splatting for Computed Tomography Guided by Convex Density Optimization

Radiative Gaussian splatting achieves high fidelity computed tomography (CT) reconstruction, but heuristic densification causes severe primitive redundancy. Pruning redundant primitives is challenging because naive removal damages subtle structural boundaries and induces streak artifacts. To resolve this issue, we propose CDGS, a structure preserving group-wise simplification framework guided by convex density optimization. By exploiting the linearity of CT projection physics under fixed geometry, we formulate candidate pricing as a convex quadratic subproblem solved via sketching. Primitives are partitioned into topological structural groups that confine density compensation within contiguous structural boundaries, which prevents non local ray interference. In addition, an autonomous in-group stopping rule dynamically terminates simplification when redundancy is exhausted. Experiments across multiple CT benchmarks demonstrate that CDGS substantially reduces primitive counts while maintaining reconstruction fidelity. Our framework outperforms existing state-of-the-art methods in both numerical accuracy and structural preservation.