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Models/V2VNet

V2VNet

Reported on 48 benchmarks across 6 tasks · 1 paper · 42 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Methodology32 results

  • 3DonV2XSet
    AP0.5 (Noisy)· 2020-08-17
    0.791
    best: 0.836 (V2X-ViT)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 3DonV2XSet
    AP0.5 (Perfect)· 2020-08-17
    0.845
    best: 0.882 (V2X-ViT)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 3DonV2XSet
    AP0.7 (Noisy)· 2020-08-17
    0.493
    best: 0.614 (V2X-ViT)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 3DonV2X-SIM
    mAOE· 2020-08-17
    0.349
    best: 0.411 (DiscoNet)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 3DonV2X-SIM
    mAP· 2020-08-17
    21.4
    best: 23.9 (QUEST)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 3DonV2X-SIM
    mASE· 2020-08-17
    0.255
    best: 0.275 (Where2comm)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 3DonV2X-SIM
    mATE· 2020-08-17
    0.768
    best: 0.911 (Where2comm)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 2D ClassificationonV2XSet
    AP0.5 (Noisy)· 2020-08-17
    0.791
    best: 0.836 (V2X-ViT)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 2D ClassificationonV2XSet
    AP0.5 (Perfect)· 2020-08-17
    0.845
    best: 0.882 (V2X-ViT)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 2D ClassificationonV2XSet
    AP0.7 (Noisy)· 2020-08-17
    0.493
    best: 0.614 (V2X-ViT)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 2D ClassificationonV2X-SIM
    mAOE· 2020-08-17
    0.349
    best: 0.411 (DiscoNet)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 2D ClassificationonV2X-SIM
    mAP· 2020-08-17
    21.4
    best: 23.9 (QUEST)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 2D ClassificationonV2X-SIM
    mASE· 2020-08-17
    0.255
    best: 0.275 (Where2comm)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 2D ClassificationonV2X-SIM
    mATE· 2020-08-17
    0.768
    best: 0.911 (Where2comm)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 2D Object DetectiononV2XSet
    AP0.5 (Noisy)· 2020-08-17
    0.791
    best: 0.836 (V2X-ViT)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 2D Object DetectiononV2XSet
    AP0.5 (Perfect)· 2020-08-17
    0.845
    best: 0.882 (V2X-ViT)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 2D Object DetectiononV2XSet
    AP0.7 (Noisy)· 2020-08-17
    0.493
    best: 0.614 (V2X-ViT)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 2D Object DetectiononV2X-SIM
    mAOE· 2020-08-17
    0.349
    best: 0.411 (DiscoNet)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 2D Object DetectiononV2X-SIM
    mAP· 2020-08-17
    21.4
    best: 23.9 (QUEST)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 2D Object DetectiononV2X-SIM
    mASE· 2020-08-17
    0.255
    best: 0.275 (Where2comm)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 2D Object DetectiononV2X-SIM
    mATE· 2020-08-17
    0.768
    best: 0.911 (Where2comm)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 16konV2XSet
    AP0.5 (Noisy)· 2020-08-17
    0.791
    best: 0.836 (V2X-ViT)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 16konV2XSet
    AP0.5 (Perfect)· 2020-08-17
    0.845
    best: 0.882 (V2X-ViT)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 16konV2XSet
    AP0.7 (Noisy)· 2020-08-17
    0.493
    best: 0.614 (V2X-ViT)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 16konV2X-SIM
    mAOE· 2020-08-17
    0.349
    best: 0.411 (DiscoNet)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 16konV2X-SIM
    mAP· 2020-08-17
    21.4
    best: 23.9 (QUEST)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 16konV2X-SIM
    mASE· 2020-08-17
    0.255
    best: 0.275 (Where2comm)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 16konV2X-SIM
    mATE· 2020-08-17
    0.768
    best: 0.911 (Where2comm)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 3DonV2XSet
    AP0.7 (Perfect)· 2020-08-17
    0.677
    best: 0.724 (V2X-AHD)
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 2D ClassificationonV2XSet
    AP0.7 (Perfect)· 2020-08-17
    0.677
    best: 0.724 (V2X-AHD)
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 2D Object DetectiononV2XSet
    AP0.7 (Perfect)· 2020-08-17
    0.677
    best: 0.724 (V2X-AHD)
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 16konV2XSet
    AP0.7 (Perfect)· 2020-08-17
    0.677
    best: 0.724 (V2X-AHD)
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519

Computer Vision16 results

  • Object DetectiononV2XSet
    AP0.5 (Noisy)· 2020-08-17
    0.791
    best: 0.836 (V2X-ViT)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • Object DetectiononV2XSet
    AP0.5 (Perfect)· 2020-08-17
    0.845
    best: 0.882 (V2X-ViT)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • Object DetectiononV2XSet
    AP0.7 (Noisy)· 2020-08-17
    0.493
    best: 0.614 (V2X-ViT)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • Object DetectiononV2X-SIM
    mAOE· 2020-08-17
    0.349
    best: 0.411 (DiscoNet)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • Object DetectiononV2X-SIM
    mAP· 2020-08-17
    21.4
    best: 23.9 (QUEST)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • Object DetectiononV2X-SIM
    mASE· 2020-08-17
    0.255
    best: 0.275 (Where2comm)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • Object DetectiononV2X-SIM
    mATE· 2020-08-17
    0.768
    best: 0.911 (Where2comm)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 3D Object DetectiononV2XSet
    AP0.5 (Noisy)· 2020-08-17
    0.791
    best: 0.836 (V2X-ViT)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 3D Object DetectiononV2XSet
    AP0.5 (Perfect)· 2020-08-17
    0.845
    best: 0.882 (V2X-ViT)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 3D Object DetectiononV2XSet
    AP0.7 (Noisy)· 2020-08-17
    0.493
    best: 0.614 (V2X-ViT)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 3D Object DetectiononV2X-SIM
    mAOE· 2020-08-17
    0.349
    best: 0.411 (DiscoNet)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 3D Object DetectiononV2X-SIM
    mAP· 2020-08-17
    21.4
    best: 23.9 (QUEST)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 3D Object DetectiononV2X-SIM
    mASE· 2020-08-17
    0.255
    best: 0.275 (Where2comm)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 3D Object DetectiononV2X-SIM
    mATE· 2020-08-17
    0.768
    best: 0.911 (Where2comm)
    SOTA
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • Object DetectiononV2XSet
    AP0.7 (Perfect)· 2020-08-17
    0.677
    best: 0.724 (V2X-AHD)
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519
  • 3D Object DetectiononV2XSet
    AP0.7 (Perfect)· 2020-08-17
    0.677
    best: 0.724 (V2X-AHD)
    V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and PredictionarXiv:2008.07519