Questions about selecting data by boxes

[LandDreamer]

Hello, your work is great! I'm trying to do some works on it. But I have some confusion about your paper and code.

1. How to select data for annoatation by a certrain number of boxes(like 200 boxes)? Is it implemented by just picking a certain number of point clouds(like 100 frames) and count the objects on them?
2. I am confusing about "requiring 1% and 8% annotations of bounding boxes and point clouds" in your paper. When I did some experiment on KITTI, I found that using 8% point clouds(300 frames) can get around 8% boxes.
3. In your code and IMPLEMENTATION DETAILS, you seem to use more data(random 100 + 6*100 CRB), which is much more than 1% and 8% annotations of bounding boxes and point clouds. I'm really confusing about it.
4. The final results vary a lot even using same query strategy(like training CRB with same parameters twice, one reult is only 79map, another one can even be close to 81), how do you solve it.

It would be much appreciated if you could answer my doubts!

[zhuoxiao-chen]

1. We pick a certain number of point clouds (e.g., 100 frames) and count the number of ground truth objects on them to annotate as the labeling cost unit.
2. In KITTI, when we use CRB to select 8% point clouds (300 / 3712 frames), the number of objects in the selected point clouds is around 1,000 out of a total of 80,256 labeled objects.
3. We have 6 rounds for active selection, and we select 100 point clouds each round. In Table 1 and Table 3, we report the results at 3rd round (8% point clouds and 1% boxes).
4. We run each experiment for 3 trials and report the averaged results. You can find the variance bar in Figure 2 and subscript for each value in Table 2, indicating a varied performance.

Hope this can help you.

[LandDreamer]

1. We pick a certain number of point clouds (e.g., 100 frames) and count the number of ground truth objects on them to annotate as the labeling cost unit.
2. In KITTI, when we use CRB to select 8% point clouds (300 / 3712 frames), the number of objects in the selected point clouds is around 1,000 out of a total of 80,256 labeled objects.
3. We have 6 rounds for active selection, and we select 100 point clouds each round. In Table 1 and Table 3, we report the results at 3rd round (8% point clouds and 1% boxes).
4. We run each experiment for 3 trials and report the averaged results. You can find the variance bar in Figure 2 and subscript for each value in Table 2, indicating a varied performance.

Hope this can help you.

Thank you for your reply!