Why 3DGS Reconstructions Fail and How a Practical QA Pipeline Fixes Them
3D Gaussian Splatting (3DGS) lacks standard benchmarks and relies on subjective visual checks, making quality assurance difficult; this article details a comprehensive, automated QA framework that evaluates input via COLMAP scores, quantifies training results with PSNR/SSIM/LPIPS, conducts subjective visual inspections, and integrates both stages into a regression pipeline to ensure reliable, scalable reconstructions.
