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Inferring CAD Modeling Sequences Using Zone Graphs

CVPR 2021 · Graph learning › Graph algorithms and clustering · approach: Neural combinatorial optimization on graphs · 50 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use zone-graph sketch + extrude search instead of primitive CSG inferenceInverseCSG test set · all modelsmedian reconstruction error (%): 0.88 -> 0.72Helped
use zone-graph sketch + extrude search instead of primitive CSG inferenceInverseCSG test set · all modelsmedian search time (s): 900 -> 242Helped
use zone-graph sketch + extrude search instead of primitive CSG inferenceInverseCSG test set · sketch + extrude modelsmedian reconstruction error (%): 0.79 -> 0.23Helped
use zone-graph sketch + extrude search instead of primitive CSG inferenceInverseCSG test set · sketch + extrude modelsmedian search time (s): 900 -> 242Helped
use network guidance instead of heuristic guidanceCAD sequence inference · InverseCSG test set, all modelsmedian reconstruction error (%): 0.50 -> 0.72Hurt
use network guidance instead of heuristic guidanceCAD sequence inference · InverseCSG test set, all modelsmedian search time (s): 197 -> 242Hurt
use network guidance instead of InverseCSG inferenceCAD sequence inference · CAD expert study, InverseCSG comparison% chosen: 84.7%Helped
use learned network guidance instead of heuristic guidanceCAD sequence inference · CAD expert study, Ours Heur comparison% chosen: 41.0%Mixed
increase extrusion proposal level from 1 to 3 (ablation)Zone graph search · Fusion 360 Galleryground-truth sequence coverage (%): 60% -> 63%Helped
increase extrusion proposal level from 1 to 3 (ablation)Zone graph search · Fusion 360 Galleryreconstruction coverage (%): 80% -> 80%No clear effect
remove zone graph simplification (ablation)Zone graph search · Fusion 360 Galleryreconstruction coverage (%): 80% -> 82%Helped

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