General Post-Training 3DGS Compression: Coordinating Quantization and Search for Quality-Constrained Size Minimization

The large file sizes of trained 3D Gaussian Splatting (3DGS) scenes increase storage and distribution costs. We propose a general post-training compression framework to reduce model size under rendering-quality constraints and a limited evaluation budget. Our key idea is to coordinate nonuniform quantization with configuration search. We apply signed-power companding to higher-order spherical harmonic coefficients within spatial blocks. This reduces quantization error and allows lower precision or larger blocks within the quality limits. The search jointly selects the pretrained base model, block size, and coefficient precision without retraining or fine-tuning. An independent view set checks the selected model against mean and tail PSNR-loss limits before delivery. Failed confirmation triggers a lossless reference fallback. Across 192 matched configurations, companding increases the number of candidates satisfying the search quality criteria from 75 to 119. At predetermined default settings on eight benchmark scenes, our complete system reduces mean model size by 97.37% and 79.60% compared with 3DGS and FastGS, respectively. Mean PSNR improves by 0.257 dB and 0.173 dB, respectively. The average rendering speed reaches 1,285.5 FPS.