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PTTR: Relational 3D Point Cloud Object Tracking With Transformer

CVPR 2022 · Point clouds › Point cloud classification · approach: Scene flow and dynamic point clouds · 123 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use PTTR instead of LTTR3D single-object tracking · KITTI tracking test, averageSuccess: 50.0 -> 58.4Helped
use PTTR instead of LTTR3D single-object tracking · KITTI tracking test, averagePrecision: 67.4 -> 77.8Helped
use PTTR instead of P2B3D single-object tracking · Waymo SOT test, averageSuccess: 40.6 -> 50.3Helped
use PTTR instead of P2B3D single-object tracking · Waymo SOT test, averagePrecision: 53.9 -> 64.9Helped
add relation-aware sampling instead of random sampling (ablation)PTTR · KITTI tracking test, averageSuccess: 52.9 -> 58.4Helped
add relation-aware sampling instead of D-FPS (ablation)PTTR · KITTI tracking test, averageSuccess: 51.4 -> 58.4Helped
add relation-aware sampling instead of F-FPS (ablation)PTTR · KITTI tracking test, averageSuccess: 54.3 -> 58.4Helped
add PRT and PRM (ablation)PTTR · KITTI tracking test, averageSuccess: 29.8 -> 58.4Helped
add PRM (ablation)PTTR with PRT · KITTI tracking test, averageSuccess: 56.7 -> 58.4Helped
use PRT rather than cosine similarity (ablation)PTTR · KITTI tracking test, averageSuccess: 50.5 -> 58.4Helped
add offset attention and feature normalization (ablation)PTTR Relation Attention · KITTI tracking test, averageSuccess: 45.6 -> 58.4Helped
add feature normalization (ablation)PTTR Relation Attention with offset attention · KITTI tracking test, averageSuccess: 52.2 -> 58.4Helped

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