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Super Fast and Accurate 3D Object Detection based on 3D LiDAR Point Clouds

Technical details of the implementation

1. Network architecture

  • The ResNet-based Keypoint Feature Pyramid Network (KFPN) that was proposed in RTM3D paper. The unofficial implementation of the RTM3D paper by using PyTorch is here

  • Input:

    • The model takes a birds-eye-view (BEV) map as input.
    • The BEV map is encoded by height, intensity, and density of 3D LiDAR point clouds. Assume that the size of the BEV input is (H, W, 3).

  • Outputs:

    • Heatmap for main center with a size of (H/S, W/S, C) where S=4 (the down-sample ratio), and C=3 (the number of classes)
    • Center offset: (H/S, W/S, 2)
    • The heading angle (yaw): (H/S, W/S, 2). The model estimates the imaginary and the real fraction (sin(yaw) and cos(yaw) values).
    • Dimension (h, w, l): (H/S, W/S, 3)
    • z coordinate: (H/S, W/S, 1)

  • Targets: 7 degrees of freedom (7-DOF) of objects: (cx, cy, cz, l, w, h, θ)

    • cx, cy, cz: The center coordinates.
    • l, w, h: length, width, height of the bounding box.
    • θ: The heading angle in radians of the bounding box.

  • Objects: Cars, Pedestrians, Cyclists.

2. Losses function

  • For main center heatmap: Used focal loss

  • For heading angle (yaw): The im and re fractions are directly regressed by using l1_loss

  • For z coordinate and 3 dimensions (height, width, length), I used balanced l1 loss that was proposed by the paper Libra R-CNN: Towards Balanced Learning for Object Detection

3. Training in details

  • Set uniform weights to the above components of losses. (=1.0 for all)
  • Number of epochs: 300.
  • Learning rate scheduler: cosine, initial learning rate: 0.001.
  • Batch size: 16 (on a single GTX 1080Ti).

4. Inference

  • A 3 × 3 max-pooling operation was applied on the center heat map, then only 50 predictions whose center confidences are larger than 0.2 were kept.
  • The heading angle (yaw) = arctan(imaginary fraction / real fraction)

5. How to expand the work

  • The model could be trained with more classes and with a larger detected area by modifying configurations in the config/kitti_dataset.py file.