Every Gaussian Counts: ReCap-GS for Resource-Constrained 3D Gaussian Splatting

3D Gaussian Splatting achieves high-quality novel-view synthesis, but its aggressive densification can make training and deployment costly under tight memory and representation-size budgets. We formulate budget-constrained 3D Gaussian Splatting as a capacity-allocation problem and introduce ReCap-GS (Recovering Capacity for Gaussian Splatting), a fixed-budget framework centered on stale-capacity recovery. Through a controlled opacity perturbation and recovery phase, ReCap-GS identifies primitives that fail to regain responsibility and reclaims their slots for subsequent demand-driven refinement. The method combines early multi-view residual-consensus growth, budget-preserving recycling, and transmittance-aware opacity redistribution while enforcing a fixed upper bound on the Gaussian population throughout training. After topology optimization has ended, we additionally adapt the Multi-view Update strategy introduced in Improved-GS. We evaluate ReCap-GS on the seven public Mip-NeRF360 scenes against a broad suite of budget-aware, pruning-based, relocation-based, and adaptive-densification baselines. Under strict budgets of 50k, 100k, and 200k Gaussians, ReCap-GS improves PSNR over Improved-GS by 0.43, 0.36, and 0.24 dB, respectively, while matching or improving SSIM and LPIPS. Compared with Improved-GS, peak VRAM is reduced by approximately 76 %, 73 %, and 65 % at these budgets, respectively. ReCap-GS remains competitive as capacity increases, suggesting that under tight budgets, recovering stale capacity is an effective complement to demand-driven representation growth.