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PAPR: Proximity Attention Point Rendering

NeurIPS 2023 · Point clouds › Point cloud classification · approach: Scene flow and dynamic point clouds · 3 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use PAPR instead of NeRFnovel-view rendering · NeRF SyntheticPSNR: 31.00 -> 32.07Helped
Use PAPR instead of Gaussian Splattingpoint-based novel-view rendering · NeRF SyntheticLPIPS_vgg: 0.084 -> 0.038Helped
Use PAPR instead of Gaussian Splattingpoint-based novel-view rendering · Tanks & TemplesPSNR: 26.81 -> 28.72Helped
Add point position p_i to key features (ablation)PAPR ray-dependent point embedding · LegoPSNR: 31.67 -> 32.62Helped
Add point position p_i to key features (ablation)PAPR ray-dependent point embedding · MicPSNR: 32.00 -> 35.64Helped
Add point position p_i and displacement t_i,j to embedding (ablation)PAPR ray-dependent point embedding · LegoPSNR: 13.26 -> 32.62Helped
Add point position p_i and displacement t_i,j to embedding (ablation)PAPR ray-dependent point embedding · MicPSNR: 18.73 -> 35.64Helped
Use more points (ablation)PAPR · NeRF SyntheticLPIPSHelped
Add point pruning (ablation)PAPR point cloud optimization · Legopoint cloud outliersHelped
Use ray-relative attention contributions normalized across points instead of absolute-distance splat contributionslearning point positions from scratch · Legogeometry reconstructionHelped

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