[{"data":1,"prerenderedAt":261},["ShallowReactive",2],{"run:bicycle-mipnerf-tuned-3m":3,"run-findings:bicycle-mipnerf-tuned-3m":239},{"run":4,"config":19,"metrics":53,"curve":59,"assets":238},{"id":5,"title":5,"subtitle":6,"eyebrow":7,"date":8,"workspace":9,"commit":10,"tags":11,"verdict":18},"bicycle-mipnerf-tuned-3m","G1 Mip-NeRF 360 実写 Pareto — bicycle 30k tuned recipe (refine.stop_iter=25000, capacity=3M)","Run summary · G1 Mip-NeRF 360 実写 Pareto v2 (killer figure #2, before\u002Fafter tuning)","2026-07-22","splat","c8c984a",[12,13,14,15,16,17],"mipnerf360","colmap","bicycle","30k","pareto","tuned","partial",[20,23,26,29,32,35,38,41,44,47,50],{"key":21,"value":22},"dataset","\u002FUsers\u002Fotkrickey\u002Fdev\u002F3dgs-workspace\u002Fdatasets\u002Fmipnerf360\u002Fbicycle",{"key":24,"value":25},"gt_convention","WhiteBg",{"key":27,"value":28},"iterations","30,000",{"key":30,"value":31},"seed","42",{"key":33,"value":34},"capacity","3,000,000 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Mip-NeRF 360 実写 Pareto v2 — bicycle before \u002F after recipe tuning","stable","mixed","experiment",[62],[12,13,14,248,249,250,16,251],"real-data","recipe-tuning","before-after","post-hoc-eval","G1 Mip-NeRF 360 (7 月実行プラン、Pareto v2)",[254,5],"bicycle-mipnerf-30k","baseline 15.549 → tuned 15.603 dB = **+0.054 dB (noise 圏)** \u002F Kerbl 2023 比 -9.65 dB (baseline とほぼ同 gap)","baseline 1h 49m → tuned 1h 42m = -7 min (±0、splats +55% を吸収)","high","case-b-scope-out-refine-capacity-not-bottleneck","\u003Cstrong>bicycle baseline (Phase I recipe 流用) = 15.549 dB \u002F Kerbl 2023 比 -9.7 dB\u003C\u002Fstrong>、tuned (refine.stop_iter=25000, capacity=3M、2 変数のみ isolated tuning) = \u003Cstrong>15.603 dB \u002F Δ +0.054 dB (実質 noise 圏)\u003C\u002Fstrong>。splats +55% (311k→483k) を追加しても PSNR は動かず、wallclock も ±0。\u003Cstrong>bottleneck は refine schedule \u002F capacity growth ではなく、より深い layer\u003C\u002Fstrong> (opacity reset cadence \u002F grad threshold \u002F unbounded outdoor scene handling)。\u003Cstrong>Case (b) scope-out 確定\u003C\u002Fstrong>: Apple Silicon native 実装の scope 境界は synthetic + object-centric に留まり、Mip-NeRF 360 実写は refine + capacity 調整では回収不能、future work として **本質的な refine schedule 再設計** or **msplat \u002F MCMC 系の densification 経路の再挑戦**が必要。\u003Cstrong>本 finding は §5.4 の MCMC 5 連敗 pattern と完全に一貫\u003C\u002Fstrong> (measurement で scope 境界を数値化、fix cost を明示化)。","\u002Ffindings\u002Fp1-mipnerf360-pareto-v2\u002F",1790227323335]