a comparative survey · Wave 6 NBV literature · tagged by what's actually comparable to our interface
There is no shared public benchmark for next-best-view over BIM/MEP scenes: the NBV field spans object-centric turntables, RL drone policies, and NeRF-uncertainty planners, each with its own scene model, action space, and metric. So a raw number-vs-number ranking would be apples-to-oranges. Below, every published method is tagged by how well it maps onto our exact interface: a partial point cloud + a candidate pose set → a per-pose score. comparable = same interface, reusable head or runnable baseline; partial = same spirit, mismatched scene/action model; off-pipeline = different inputs (RGB/NeRF) or output (RL actions, not candidate scores).
compute_mep_recall originally divided captured instances by the instances visible in the partial cloud: using the scanned cloud as its own ground truth. Observing one instance scored 1.0; oracle inflated 5-50×. Re-anchoring to scene-full GT (scene.instance_class) dropped oracle on gni_model_173 from 1.0 → 0.0093. Any NBV result that scores recall/coverage against a partial reconstruction's own labels is exposed to the same inflation: worth checking before trusting cross-paper numbers.octomap_ig baseline; the learned-ranker-plus-lookahead hybrid beats it on every split. Everything else is the right idea on the wrong scene model (NBV-Net's 32³ grid + fixed 14-view sphere; MA-SCVP's object-centric turntable) or a different problem entirely (NeU-NBV needs a per-scene NeRF; GenNBV samples RL actions, not candidate scores).The OOD win came from more diverse buildings, not a cleverer loss, so Wave 6 surveyed open BIM/IFC corpora. IFC-Bench v2 was used (93 sub-scenes added); the rest are documented leads for further expansion.