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A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion

ICLR 2022 · Point clouds › Shape and surface completion · approach: Structure-guided completion · 8 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use conditional DDPM to generate coarse point clouds and refine them with RFNetPDR · MVPEMD: 1.37 vs. 2.31Helped
Use conditional DDPM to generate coarse point clouds and refine them with RFNetPDR · MVP-40 (50% missing)EMD: 2.68 vs. 18.40Helped
Use conditional DDPM to generate coarse point clouds and refine them with RFNetPDR · MVP-40 (12.5% missing)EMD: 1.39 vs. 4.54Helped
Use conditional DDPM to generate coarse point clouds and refine them with RFNetPDR · Completion3DEMD: 1.75 vs. 2.69Helped
Add attention to PA-Deconv (ablation)PDR CGNet · MVP, coarse generationCD: 8.81 -> 8.71Helped
Add PA-Deconv to PointNet++ (ablation)PDR CGNet · MVP, coarse generationCD: 9.39 -> 8.81Helped
Use PA-Deconv and attention instead of PointNet++ (ablation)PDR CGNet · MVP, coarse generationCD: 9.39 -> 8.71Helped
Replace FT with concatenating noisy and partial clouds (ablation)PDR CGNet · MVP, coarse generationCD: 8.71 -> 10.79Hurt
Use only a global conditioner feature (ablation)PDR CGNet · MVP, coarse generationCD: 8.71 -> 11.99Hurt
Add attention to PA-Deconv (ablation)PDR RFNet · MVP, refinementCD: 5.96 -> 5.66Helped
Add PA-Deconv to PointNet++ (ablation)PDR RFNet · MVP, refinementCD: 6.03 -> 5.96Helped
Replace FT with concatenating noisy and partial clouds (ablation)PDR RFNet · MVP, refinementCD: 5.66 vs. 5.78Mixed

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