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Argoverse: 3D Tracking and Forecasting With Rich Maps

CVPR 2019 · 3D vision › 3D generation · approach: 3D perception for autonomous driving · 1,487 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use map-based ground removal instead of plane-fitting (ablation)Baseline tracker · Argoverse 3D Tracking test, 50 mMOTA: 49.3 -> 50.0Helped
use map-based ground removal instead of plane-fitting (ablation)Baseline tracker · Argoverse 3D Tracking test, 100 mMOTP-I: 0.28 -> 0.25Helped
use map-based ground removal instead of plane-fitting (ablation)Baseline tracker · Argoverse 3D Tracking test, 30 mMOTP-O: 13.7 -> 15.3Hurt
add lane-direction orientation initialization (ablation)Baseline tracker · Argoverse 3D Tracking test, 100 mMOTP-O: 27.7 -> 13.3Helped
add social features to LSTM inputs (ablation)LSTM · Argoverse Forecasting, K=1minADE: 2.15 -> 2.15No clear effect
add social features to LSTM inputs (ablation)LSTM · Argoverse Forecasting, K=1minFDE: 4.97 -> 4.95No clear effect
prune nearest-neighbor predictions that leave the drivable areaNN · Argoverse Forecasting, K=6minFDE: 3.29 -> 3.19Helped
use driveable-area pruningNN · Argoverse Forecasting, K=6DAC: 0.87 -> 0.94Helped
use lane-centerline coordinates and map priorNN · Argoverse Forecasting, K=1minFDE: 7.88 -> 8.12Hurt
use lane-centerline coordinates and map priorNN · Argoverse Forecasting, K=6DAC: 0.87 -> 0.96Helped
allow multiple predictions along each centerline rather than one (ablation)NN+map(prior) · Argoverse Forecasting, K=6minFDE: 5.32 -> 4.02Helped
use an LSTM instead of nearest-neighbor regression with map priorMap-prior forecasting · Argoverse Forecasting, K=6minFDE: 5.32 -> 4.19Helped

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