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CubifAE-3D: Monocular Camera Space Cubification on Autonomous Vehicles for Auto-Encoder based 3D Object Detection

October 2020

tl;dr: Use depth pretraining with AE on synthetic data to help Mono3D.

Overall impression

The idea of using depth pretraining for mono3D is similar to Geometric pretraining for monoDepth. The pretraining can be done with synthetic dataset. Maybe the self-supervised pretraining can also work.

The idea of cubifying 3D space is densely sample 3D space, and is similar to the idea of 3D anchors in M3D-RPN. The idea of pool 2D image features into 3D voxels resembles that of OFT.

The most contribution to this work seems to be the improvement of 3D detection for far away objects. It in a way eliminated the depth dependency of prediction errors. --> How is this done?

The GT assignment looks interesting as it predicts up to 10 cars in each cuboid and they are sorted by increasing depth. Anchor is a way to implicitly sorting the prediction and GT. DETR is quite radical in eliminating the ordering and sorting of GT and prediction altogether and replace with a Hungarian matching loss.

Key ideas

  • Pretraining monoDepth with synthetic dataset.
    • U-Net like structure, with MSE error and edge aware smoothing error.
    • The pretrained weights from depth encoder is frozen, and the depth decoder is discarded.
    • The networks learns the linear relationships between relative focal lengths and relative depth scales between simulation and real datasets, regardless of differences in camera intrinsics.
  • Cubify the entire place into (2x2)x5 depth bin x 10 objects per bin.
    • predict 10 object per cuboid, in increasing order of z-depth from ego vehicle.
    • Each quadrant of the image with depth limit is one cuboid.
  • Loss
    • whl loss: L2 of sqrt diff
    • xyz loss: L2 loss
    • orientation loss: L2 loss
    • conf loss: L2 loss
    • iou loss: introducing this loss speeds up the stabilizes the training process.
  • Classifier: a separate classifier network to crop the patches out with the predicted whl to predict vehicle class.

Technical details

  • Photometric data aug: RGB to HSV first, then adjust S and V.
  • Geometric data aug: projective data aug, not done yet as it requires determination of new object pose.

Notes

  • Q: is it possible to use single image for depth pretraining? The idea would be similar to Virtual Cam/Movi-3D and Cam Conv, by augmenting single image and its depth value.