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Models/PillarNeXt

PillarNeXt

Reported on 18 benchmarks across 6 tasks · 1 paper · 6 SOTA

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

Methodology12 results

3Donwaymo vehicle
APH/L2· 2023-05-08
75.76
SOTA
PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds arXiv:2305.04925
2D Classificationonwaymo vehicle
APH/L2· 2023-05-08
75.76
SOTA
PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds arXiv:2305.04925
2D Object Detectiononwaymo vehicle
APH/L2· 2023-05-08
75.76
SOTA
PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds arXiv:2305.04925
16konwaymo vehicle
APH/L2· 2023-05-08
75.76
SOTA
PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds arXiv:2305.04925
3Donwaymo cyclist
APH/L2· 2023-05-08
70.55
best: 78 (DSVT(val))
PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds arXiv:2305.04925
3Donwaymo pedestrian
APH/L2· 2023-05-08
75.98
best: 76.4 (DSVT(val))
PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds arXiv:2305.04925
2D Classificationonwaymo cyclist
APH/L2· 2023-05-08
70.55
best: 78 (DSVT(val))
PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds arXiv:2305.04925
2D Classificationonwaymo pedestrian
APH/L2· 2023-05-08
75.98
best: 76.4 (DSVT(val))
PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds arXiv:2305.04925
2D Object Detectiononwaymo cyclist
APH/L2· 2023-05-08
70.55
best: 78 (DSVT(val))
PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds arXiv:2305.04925
2D Object Detectiononwaymo pedestrian
APH/L2· 2023-05-08
75.98
best: 76.4 (DSVT(val))
PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds arXiv:2305.04925
16konwaymo cyclist
APH/L2· 2023-05-08
70.55
best: 78 (DSVT(val))
PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds arXiv:2305.04925
16konwaymo pedestrian
APH/L2· 2023-05-08
75.98
best: 76.4 (DSVT(val))
PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds arXiv:2305.04925

Computer Vision6 results

Object Detectiononwaymo vehicle
APH/L2· 2023-05-08
75.76
SOTA
PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds arXiv:2305.04925
3D Object Detectiononwaymo vehicle
APH/L2· 2023-05-08
75.76
SOTA
PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds arXiv:2305.04925
Object Detectiononwaymo cyclist
APH/L2· 2023-05-08
70.55
best: 78 (DSVT(val))
PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds arXiv:2305.04925
Object Detectiononwaymo pedestrian
APH/L2· 2023-05-08
75.98
best: 76.4 (DSVT(val))
PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds arXiv:2305.04925
3D Object Detectiononwaymo cyclist
APH/L2· 2023-05-08
70.55
best: 78 (DSVT(val))
PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds arXiv:2305.04925
3D Object Detectiononwaymo pedestrian
APH/L2· 2023-05-08
75.98
best: 76.4 (DSVT(val))
PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds arXiv:2305.04925