demo.md

Firstly you need to prepare the dataset and pre-trained models as described here.

Then modify the directories by

cd ${EgoNet_DIR}/configs && vim KITTI_inference:demo.yml

Edit dirs:ckpt to your pre-trained model directory.

Edit dataset:root to your KITTI directory.

Finally, go to ${EgoNet_DIR}/tools and run

 python inference.py --cfg "../configs/KITTI_inference:demo.yml" --visualize True --batch_to_show 2

You can set --batch_to_show to other integers to see more results.

The visualized 3D bounding boxes are distinguished by their colors:

1. Black indicates ground truth 3D boxes.
2. Magenta indicates 3D bounding boxes predicted by another 3D object detector (D4LCN).
3. Red indicates the predictions of Ego-Net, using the 2D bounding boxes from D4LCN.
4. Yellow indicates the predictions of Ego-Net, using the ground truth 2D bounding boxes.