Gradient-Driven Natural Selection for Compact 3D Gaussian Splatting

Compact 3D Gaussian Splatting must reduce the number of primitives while preserving reconstruction quality. In a dense representation, overlapping Gaussians jointly support local details, so a member's current contribution may not reflect its value after other members leave and the scene is reoptimized. We introduce Gradient-Driven Natural Selection (GNS), which organizes competition through shrinkage and reconstruction-driven adaptation of native opacity, allowing necessary local support to concentrate on fewer members before pruning. GNS applies a shared linear penalty to pre-activation opacity parameters to reduce selection bias introduced by the shrinkage term itself. It also increases the opacity learning rate during selection so that members respond to reconstruction feedback as the representation contracts. The surviving subset emerges during reconstruction optimization without extra learnable selection variables or inference modules. With common dense inputs, training iterations, and target Gaussian budgets, these two simple changes achieve better overall reconstruction quality than the compared learned pruning baselines across multiple datasets.