RASS: Risk-Audited Budget Selection for Compact NeRF Benchmark Subsets

Full-dataset NeRF evaluation can require training and evaluating thousands of scenes, making exhaustive comparison expensive. In this paper, we introduce RASS, a risk-audited protocol for constructing compact NeRF benchmark subsets with explicit fidelity criteria. Given full-dataset per-scene logs, RASS repeatedly samples candidate subsets, checks whether their global metric means and score distributions match the full benchmark within user-defined tolerances, and recommends the smallest evaluated subset size whose Wilson lower confidence bound exceeds a target pass probability. We instantiate RASS on 3,522 Nutrition5k-derived food scenes using Zip-NeRF logs for the formal audit and PSNR, SSIM, LPIPS, and Kolmogorov--Smirnov (KS) distributional tests as fidelity criteria. The released 48-scene balanced operating point, RASS-48, is the lowest-cost evaluated screening subset that passes our declared global-fidelity audit and is intended for low-cost early ablations and rapid screening. A stricter target pass probability, $p_{\min}{=}0.20$, selects RASS-96, which we recommend when a stronger compact evaluation setting is affordable. We also evaluate alternative subset generators, including uniform random sampling, farthest-first selection, and facility-location diversity sampling, and find that the RASS audit can validate different generators under the same fidelity contract. Cross-method checks on Instant-NGP and Feature-Splatting are reported as diagnostic evidence rather than formal guarantees. We release the RASS-48/96 scenes, configurations, descriptors, regime labels, seeds, audit scripts, and permitted logs to enable reproducible, compact NeRF evaluation.