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Self-Positioning Point-Based Transformer for Point Cloud Understanding

CVPR 2023 · Point clouds › Point cloud classification · approach: Self-supervised point cloud pretraining · 123 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use SPoTr instead of the strongest listed baselineShape classification on SONN_PB_T50_RS · SONN_PB_T50_RSmAcc: 85.8±0.6 -> 86.8Helped
use SPoTr instead of the strongest listed baselineShape classification on SONN_PB_T50_RS · SONN_PB_T50_RSOA: 87.7±0.4 -> 88.6Helped
use SPoTr instead of PointNeXtPart segmentation · SN-Partins. mIoU: 87.0 $\pm$ 0.1 -> 87.2Helped
use SPoTr instead of PointNeXtSemantic segmentation · S3DIS Area-5mIoU: 70.5 ± 0.3 -> 70.8Helped
add SPA to local point attention (ablation)SPoTr on SONN_PB · SONN_PBOA: 87.9 -> 88.6Helped
use adaptive self-positioning points instead of FPS-selected points (ablation)SPoTr on SONN_PB · SONN_PBOA: 87.7 -> 88.6Helped
add disentangled spatial and semantic attention instead of using only the spatial kernel (ablation)SPoTr on SONN_PB · SONN_PBOA: 88.2 -> 88.6Helped
use CWPA instead of standard attention (ablation)SPoTr on SONN_PB · SONN_PBOA: 86.1 -> 88.6Helped
use f_k as the semantic relation instead of f_q - f_k (ablation)CWPA in SPoTr · SONN_PBOA: 88.6 -> 88.1Hurt
use f_q + f_k as the semantic relation instead of f_q - f_k (ablation)CWPA in SPoTr · SONN_PBOA: 88.6 -> 86.4Hurt
use f_q ⊙ f_k as the semantic relation instead of f_q - f_k (ablation)CWPA in SPoTr · SONN_PBOA: 88.6 -> 85.4Hurt

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