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July 2020

tl;dr: CenterPoint with bells and whistles wins the 2020 Waymo Open Dataset challenge.

Overall impression

The anchor-free object detection of AFDet is very close to CenterPoint.

Key ideas

  • Anchor free, NMS free. 1000x faster on CPU. Embedded system friendly.
  • Architecture
    • PointPillars backbone.
    • The backbone only has two stage network and keeps the same high resolution for object detection. No FPN needed as in BEV only one scale is needed.
    • Same feature map size as input size. Do not downsize.
  • Lidar pseudo-image are more sparse than natural images.
    • Larger kernel and more non-zero pixels than Gaussian. Fill in all pixels inside bbox to a small value. --> CenterPoint enlarged gaussian kernel as well.
    • More pixels contribute to offset regression.
  • Offset branch not only corrects quantization error, but can also correct regression error. 5x5 pixels to regress offset, as compared to only 1 pixel regressing offset in CenterNet.
  • Data augmentation:
    • Create a bank of objects
    • randomly select 15 GT samples for car/vehicle and place them into the current point cloud.
    • Each object is rotated [-9, 9] deg
    • global rotation [-45, 45] deg
  • Bag of tricks
    • High resolution input matters
    • High resolution feature map helps
    • AdamW + 1 cycle policy for super convergence.
    • Data aug during training
  • Tricks for winning solution (not in this paper)
    • Densification (with pervious 4 frames)
    • pointpainting (2D bbox painting and segmentation painting). 50% painted as waymo does not have a rear view cam
    • train 3 models and perform ensemble and TTA
    • Merging of TTA and ensemble bbox with weighted bbox fusion

Technical details

  • 8 bits for orientation (2 bins x (cos + sin + 2-bit cls) per bin) --> This is same as CenterNet.
  • Waymo and KITTI uses the same 0.7 3D IoU as the KPI.

Notes