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Models/F-PointNet++ [Qi:2018fd]

F-PointNet++ [Qi:2018fd]

Reported on 36 benchmarks across 6 tasks · 1 paper · 36 SOTA

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

Methodology24 results

3DonKITTI Cyclist Moderate val
AP· 2017-11-22
56.49
best: 71.7 (M3DeTR)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
3DonKITTI Cyclist Easy val
AP· 2017-11-22
77.15
best: 89.13 (M3DeTR)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
3DonKITTI Cyclist Hard val
AP· 2017-11-22
53.37
best: 68.29 (M3DeTR)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
3DonKITTI Pedestrian Moderate val
AP· 2017-11-22
61.32
best: 64.71 (PVCNN)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
3DonKITTI Pedestrian Hard val
AP· 2017-11-22
53.59
best: 56.78 (PVCNN)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
3DonKITTI Pedestrian Easy val
AP· 2017-11-22
70
best: 73.2 (PVCNN)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
2D ClassificationonKITTI Cyclist Moderate val
AP· 2017-11-22
56.49
best: 71.7 (M3DeTR)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
2D ClassificationonKITTI Cyclist Easy val
AP· 2017-11-22
77.15
best: 89.13 (M3DeTR)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
2D ClassificationonKITTI Cyclist Hard val
AP· 2017-11-22
53.37
best: 68.29 (M3DeTR)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
2D ClassificationonKITTI Pedestrian Moderate val
AP· 2017-11-22
61.32
best: 64.71 (PVCNN)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
2D ClassificationonKITTI Pedestrian Hard val
AP· 2017-11-22
53.59
best: 56.78 (PVCNN)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
2D ClassificationonKITTI Pedestrian Easy val
AP· 2017-11-22
70
best: 73.2 (PVCNN)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
2D Object DetectiononKITTI Cyclist Moderate val
AP· 2017-11-22
56.49
best: 71.7 (M3DeTR)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
2D Object DetectiononKITTI Cyclist Easy val
AP· 2017-11-22
77.15
best: 89.13 (M3DeTR)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
2D Object DetectiononKITTI Cyclist Hard val
AP· 2017-11-22
53.37
best: 68.29 (M3DeTR)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
2D Object DetectiononKITTI Pedestrian Moderate val
AP· 2017-11-22
61.32
best: 64.71 (PVCNN)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
2D Object DetectiononKITTI Pedestrian Hard val
AP· 2017-11-22
53.59
best: 56.78 (PVCNN)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
2D Object DetectiononKITTI Pedestrian Easy val
AP· 2017-11-22
70
best: 73.2 (PVCNN)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
16konKITTI Cyclist Moderate val
AP· 2017-11-22
56.49
best: 71.7 (M3DeTR)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
16konKITTI Cyclist Easy val
AP· 2017-11-22
77.15
best: 89.13 (M3DeTR)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
16konKITTI Cyclist Hard val
AP· 2017-11-22
53.37
best: 68.29 (M3DeTR)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
16konKITTI Pedestrian Moderate val
AP· 2017-11-22
61.32
best: 64.71 (PVCNN)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
16konKITTI Pedestrian Hard val
AP· 2017-11-22
53.59
best: 56.78 (PVCNN)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
16konKITTI Pedestrian Easy val
AP· 2017-11-22
70
best: 73.2 (PVCNN)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488

Computer Vision12 results

Object DetectiononKITTI Cyclist Moderate val
AP· 2017-11-22
56.49
best: 71.7 (M3DeTR)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
Object DetectiononKITTI Cyclist Easy val
AP· 2017-11-22
77.15
best: 89.13 (M3DeTR)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
Object DetectiononKITTI Cyclist Hard val
AP· 2017-11-22
53.37
best: 68.29 (M3DeTR)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
Object DetectiononKITTI Pedestrian Moderate val
AP· 2017-11-22
61.32
best: 64.71 (PVCNN)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
Object DetectiononKITTI Pedestrian Hard val
AP· 2017-11-22
53.59
best: 56.78 (PVCNN)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
Object DetectiononKITTI Pedestrian Easy val
AP· 2017-11-22
70
best: 73.2 (PVCNN)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
3D Object DetectiononKITTI Cyclist Moderate val
AP· 2017-11-22
56.49
best: 71.7 (M3DeTR)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
3D Object DetectiononKITTI Cyclist Easy val
AP· 2017-11-22
77.15
best: 89.13 (M3DeTR)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
3D Object DetectiononKITTI Cyclist Hard val
AP· 2017-11-22
53.37
best: 68.29 (M3DeTR)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
3D Object DetectiononKITTI Pedestrian Moderate val
AP· 2017-11-22
61.32
best: 64.71 (PVCNN)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
3D Object DetectiononKITTI Pedestrian Hard val
AP· 2017-11-22
53.59
best: 56.78 (PVCNN)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488
3D Object DetectiononKITTI Pedestrian Easy val
AP· 2017-11-22
70
best: 73.2 (PVCNN)
SOTA
Frustum PointNets for 3D Object Detection from RGB-D Data arXiv:1711.08488