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PatchFormer: An Efficient Point Transformer With Patch Attention

CVPR 2022 · Point clouds › Point cloud classification · approach: Efficient point sampling architectures · 88 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use 32 bases instead of 96 (ablation)PatchFormer · ModelNet40OA: 93.52 -> 91.54Hurt
Use 64 bases instead of 96 (ablation)PatchFormer · ModelNet40OA: 93.52 -> 92.94Hurt
Use 128 bases instead of 96 (ablation)PatchFormer · ModelNet40OA: 93.52 -> 93.50No clear effect
Use 128 rather than 96 bases (ablation)PatchFormer · ModelNet40latency: 34.32ms -> 35.56msHurt
Remove multi-scale feature aggregation (ablation)PatchFormer · ModelNet40OA: 93.52 -> 92.85Hurt
Remove multi-scale feature aggregation (ablation)PatchFormer · ShapeNetmIoU: 86.52 -> 85.22Hurt
Replace PAT with MLP (ablation)PatchFormer · ModelNet40OA: 93.52 -> 92.62Hurt
Replace PAT with MLP (ablation)PatchFormer · ShapeNetmIoU: 86.52 -> 85.32Hurt
Replace PAT with self-attention (ablation)PatchFormer · ModelNet40OA: 93.52 -> 93.29Hurt
Replace PAT with self-attention (ablation)PatchFormer · ShapeNetmIoU: 86.52 -> 86.22Hurt
Remove 3D relative position bias (ablation)PatchFormer · ModelNet40OA: +0.37% OAHurt
Remove 3D relative position bias (ablation)PatchFormer · ShapeNetmIoU: +0.47% mIoUHurt

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