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PolyDiffuse: Polygonal Shape Reconstruction via Guided Set Diffusion Models

NeurIPS 2023 · Image generation › Fast diffusion sampling · approach: Diffusion posterior sampling · 8 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add guided set diffusion refinement to RoomFormer* proposalsRoomFormer* · Structured3D testAngle F1: 83.9 -> 89.1Helped
Add guided set diffusion refinement to MapTR+ proposalsMapTR+ · nuScenesOrdered-angle mAP: 43.8 -> 49.0Helped
Add guided set diffusion refinement to MapTR+ proposalsMapTR+ · nuScenesChamfer mAP: 59.3 -> 59.7No clear effect
Use rough annotations rather than MapTR proposalsPolyDiffuse · nuScenesOrdered-angle mAP: 49.0 -> 45.3Hurt
Remove guidance networks and proposal initialization, using standard DM (ablation)PolyDiffuse · Structured3D testAngle F1: 89.1 -> 81.1Hurt
Reduce standard DM sampling from 10 to 2 steps (ablation)Standard DM · Structured3D testAngle F1: 81.1 -> 5.4Hurt
Increase PolyDiffuse training schedule from 1× to 2.5× (ablation)PolyDiffuse · Structured3D testRoom F1: 98.0 -> 98.4Helped
Add intra-element attention to the denoising decoder (ablation)PolyDiffuse · Structured3D testAngle F1: 88.6 -> 89.1Helped
Use rough annotations instead of RoomFormer* proposalsPolyDiffuse · Structured3D testAngle F1: 83.9 -> 89.4Helped

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