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Models/Frustum VoxNet v2 (FPN + 3D ResNetFCN6 V2)

Frustum VoxNet v2 (FPN + 3D ResNetFCN6 V2)

Reported on 12 benchmarks across 6 tasks

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

Methodology8 results

  • 3DonSUN-RGBD val
    Inference Speed (s)
    0.24
    best: 0.048 (Frustum VoxNet (YOLO v3+3D ResNetFCN6))
  • 3DonSUN-RGBD val
    mAP@0.25
    45
    best: 69.7 (Point-GCC+TR3D+FF)
  • 2D ClassificationonSUN-RGBD val
    Inference Speed (s)
    0.24
    best: 0.048 (Frustum VoxNet (YOLO v3+3D ResNetFCN6))
  • 2D ClassificationonSUN-RGBD val
    mAP@0.25
    45
    best: 69.7 (Point-GCC+TR3D+FF)
  • 2D Object DetectiononSUN-RGBD val
    Inference Speed (s)
    0.24
    best: 0.048 (Frustum VoxNet (YOLO v3+3D ResNetFCN6))
  • 2D Object DetectiononSUN-RGBD val
    mAP@0.25
    45
    best: 69.7 (Point-GCC+TR3D+FF)
  • 16konSUN-RGBD val
    Inference Speed (s)
    0.24
    best: 0.048 (Frustum VoxNet (YOLO v3+3D ResNetFCN6))
  • 16konSUN-RGBD val
    mAP@0.25
    45
    best: 69.7 (Point-GCC+TR3D+FF)

Computer Vision4 results

  • Object DetectiononSUN-RGBD val
    Inference Speed (s)
    0.24
    best: 0.048 (Frustum VoxNet (YOLO v3+3D ResNetFCN6))
  • Object DetectiononSUN-RGBD val
    mAP@0.25
    45
    best: 69.7 (Point-GCC+TR3D+FF)
  • 3D Object DetectiononSUN-RGBD val
    Inference Speed (s)
    0.24
    best: 0.048 (Frustum VoxNet (YOLO v3+3D ResNetFCN6))
  • 3D Object DetectiononSUN-RGBD val
    mAP@0.25
    45
    best: 69.7 (Point-GCC+TR3D+FF)