METHODOLOGY.md

# Methodology and Reproducibility

## Datasets
- Game-depth (ours): 48,615 RGB (1080x1920) plus dense engine z-buffer depth (log-nearness). Training uses
  a session-balanced split (at most 200 frames/session, 17,799 frames), stride-sampled to reduce 3-fps
  temporal redundancy. Splits are session-disjoint. Held-out game test set: 999 session-disjoint frames.
- NYU Depth V2 (real, indoor): official BTS split, 24,231 train frames
  (`nyudepthv2_train_files_with_gt.txt`), 654 Eigen test. Depth = uint16 PNG / 1000 m; valid = png > 0.
- KITTI (real, outdoor): depth-selection `val_selection_cropped`, 1000 frames, 352x1216, annotated
  semi-dense GT. Depth = uint16 PNG / 256 m; valid = png > 0; eval cap 80 m.

## Model and training recipe
- Architecture and recipe: Lotus (latent diffusion, SD2-base backbone; single-step x0 at t=999; RGB+depth
  latent concat, 8-channel conv_in; `trunc_disparity` normalization). Recipe used verbatim; only the data
  reader differs.
- Game pre-training (our model): init SD2-base; effective batch 32; 6000 steps; LR 3e-5 constant.
- NYU fine-tuning: identical recipe for both initializations (our game model, released Lotus). Both are
  evaluated under a learning-rate sweep (best-of checkpoints saved every 750 steps). A first comparison at
  LR 3e-5 suggested a game-pretraining advantage, but a fair LR sweep showed the Lotus baseline had been
  under-tuned (best LR-1e-5 checkpoint reaches AbsRel 0.115). The honest conclusion is that the indoor
  fine-tuning difference is a training-recipe effect, not a data effect; the matched sweep for our own model
  is being finalized.

## Evaluation harness (all models, one protocol)
- Inference: LotusGPipeline for the diffusion models, single-step, task-emb depth.
- Per-model output conventions handled explicitly: ours and Lotus predict nearness (a disparity proxy),
  Marigold predicts affine-invariant depth, Depth-Anything-V2 predicts inverse depth.
- Per-model inference settings, disclosed for fairness: ours and Lotus single-step; Marigold 10 steps with
  ensemble size 1 (single-sample, not its multi-sample best); Depth-Anything-V2 a single forward pass.
- Resolution: NYU and game frames processed at long-side 768. KITTI processed at native resolution, because
  its roughly 3.4:1 frames are otherwise squashed to about 224 px tall and blurred; native processing
  improves every model and is the pinned KITTI protocol.
- Metrics: AbsRel, SqRel, RMSE, RMSElog, log10, delta1/2/3, plus SSI-MAE and boundary-F1 for the game
  held-out set.
- Cropping and caps: NYU Eigen crop, cap 10 m. KITTI no extra crop (val set is pre-cropped), cap 80 m.
- Alignment: least-squares scale-shift to the ground truth (predictions are relative).
- Harness validation: Depth-Anything-V2 reproduces its published NYU AbsRel (about 0.055) in this harness,
  so the relative ordering across models is trustworthy even though single-step numbers differ from each
  paper's own protocol.

## Analysis
- Appearance (DINOv2 Frechet distance): frozen DINOv2 ViT-S/14 embeddings, L2-normalized; Frechet distance
  between Gaussian-fit embedding sets (game training split vs NYU vs KITTI). Preprocessing is
  aspect-preserving center-crop to 224 (a naive square resize was tested and rejected because it distorts
  wide KITTI). This is a semantic, not photometric, similarity measure and is treated as one correlational
  signal, not proof.
- Depth geometry (ground-plane strength): for each image, Spearman rank correlation rho between pixel row
  and pixel distance over valid pixels (subsampled to 4000 px/image); we report the per-image rho
  distribution (median and mean). Distance is depth (m) for real sets and 1 minus nearness for the game
  data. Being rank-based and per-image, rho is scale-invariant and unaffected by KITTI's sky-crop.
- Causal test (in progress): keep the game RGB fixed, progressively flatten the depth target to destroy the
  ground-plane geometry, retrain from scratch at each corruption level, and measure KITTI transfer. If
  transfer degrades as geometry is destroyed, the geometry is the causal driver rather than a confound.

## KITTI win robustness
The KITTI advantage is checked against the concern that a metric win could reflect fitting sensor noise or a
trivial ground plane. Recomputing the win under stricter valid masks refutes this: it grows when boundary
and isolated LiDAR pixels are eroded (+0.074 AbsRel gap) and at long range beyond 20 m (+0.086), and is
neutral only on upper-image vertical structures. That is the signature of a genuine ground-plane advantage.

## Files
- `results/results_summary.json`: headline metrics.
- `results/geometry_analysis.json` and `results/geo_rho_per_image.json`: geometry rho.
- `results/fid_recheck.json` and `results/domain_analysis.json`: DINOv2 Frechet distance.
- `figures/geometry_profile.png`: per-image ground-plane rho distribution.

## Known caveats
- Indoor is out of distribution (0% indoor in training); the NYU zero-shot gap reflects data distribution.
- Depth GT is relative log-nearness, not metric; cross-dataset comparisons are done in normalized space.
- Point estimates are single-seed; multi-seed variance and bootstrap confidence intervals are being added.
- Marigold and Depth-Anything-V2 are external checkpoints trained under conditions we do not control, so
  they are reference points rather than controlled comparisons; the controlled comparison is ours vs Lotus.