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Research map / 3D vision / 3D generation

Neural Part Priors: Learning To Optimize Part-Based Object Completion in RGB-D Scans

CVPR 2023 · 3D vision › 3D generation · approach: Open-vocabulary 3D scene understanding · 5 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use NPP instead of Bokhovkin et al.Part decomposition and completion · Scan2CAD (ScanNet)Chamfer Distance accuracy: 0.108 -> 0.074Helped
Use NPP instead of Bokhovkin et al.Part decomposition and completion · Scan2CAD (ScanNet)Chamfer Distance completion: 0.110 -> 0.092Helped
Add latent projection mapping initialization (ablation)NPP · Scan2CAD (ScanNet)Chamfer Distance accuracy: 0.160 -> 0.074Helped
Add synthetic pretraining (ablation)NPP · Scan2CAD (ScanNet)Chamfer Distance completion: 0.122 -> 0.092Helped
Add test-time optimization (ablation)NPP · Scan2CAD (ScanNet)Chamfer Distance accuracy: 0.093 -> 0.074Helped
Add full-shape constraint (ablation)NPP test-time optimization · Scan2CAD (ScanNet)Chamfer Distance completion: 0.101 -> 0.092Helped
Add dense part segmentation guidance (ablation)NPP test-time optimization · Scan2CAD (ScanNet)Chamfer Distance completion: 0.112 -> 0.092Helped
Add scene-consistency optimization (ablation)NPP · Scan2CAD (ScanNet)Chamfer Distance accuracy: 0.077 -> 0.074Helped

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