Every stage from an IFC building model to a surface-coverage recall@K number: pose sampling, raycast capture, the K-step rollout, the joint learned scorer, the lookahead wrapper that actually ships, and the metric. Click any node for what it is, the real class/function behind it, and the committed file path.
The two amber FIX nodes are the methodology contribution: scanner-z anchoring + raycast floor filter in pose sampling, and the scene-full GT denominator in the recall metric. The second is a metric pathology that inflated oracle 5-50×. Fixing it dropped oracle on gni_model_173 from 1.0 to 0.0093.
data / training flow derived signal / eval● win · headlineFIX · methodology contribution
Eval protocol: surface-coverage recall@K=1 · 5 seeds · n_cand=20 · hybrid_top_k=12 · mean ± SE across seeds. Hybrid = LearnedPlusLookaheadBaseline (model scores M candidates, takes top-K=12, runs 2-step lookahead). Classical baseline: OctoMap-IG (Bircher et al., ICRA 2016). The two FIX nodes (scanner-z anchoring + raycast floor filter, and the scene-full GT denominator) are the methodology contribution.