a comparative survey · 32 NBV papers · tagged by what is 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 plus a candidate pose set, returning a per-pose score. comparable means same interface, reusable head or runnable baseline; partial means same spirit, mismatched scene/action model; off-pipeline means 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×. We caught it because a newly-added extractor scored below random (random was inflated, the extractor was fine), and re-anchoring to scene-full GT (scene.instance_class) dropped oracle on gni_model_173 from 1.0 to 0.0093. We then surveyed the NBV literature to check whether this is a shared pitfall. The field standard is to normalize recall/coverage against the complete object or scene ground truth: object-reconstruction NBV (PC-NBV, NBV-Net, SCVP, GenNBV), info-gain NBV, semantic/instance NBV, and as-built/TLS scan planning all already do this. Across 32 papers we found no prior work with this pathology; the closest analog is the explicitly disclosed no-GT fallback in measurement-direct NBV (Surface Edge Explorer). So this was a real trap in our code, but we make no claim that other people's published numbers are affected (no contamination claimed).Tagged against our interface: a (partial cloud, candidate poses) input that returns a per-pose score.
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 plus 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.