You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
I'm looking into the monocular 3D object detection on Waymo Open Dataset.
In your paper, you showed results in this table:
In the recent CVPR paper, "MonoJSG: Joint Semantic and Geometric Cost Volume for Monocular 3D Object Detection", they summarized the results in this table:
Their test set results on KITTI seem to be better than CaDDN.
All in all, your CaDDN achieves 5% AP at 0.7 IoU whereas MonoJSG is barely 1% AP at the same threshold. I believe both of your methods used the same setting, i.e., front cameras and vehicle class.
Do you have any ideas for where the difference might come from?
Thanks a lot!
The text was updated successfully, but these errors were encountered:
Hi,
I'm looking into the monocular 3D object detection on Waymo Open Dataset.
In your paper, you showed results in this table:
In the recent CVPR paper, "MonoJSG: Joint Semantic and Geometric Cost Volume for Monocular 3D Object Detection", they summarized the results in this table:
Their test set results on KITTI seem to be better than CaDDN.
All in all, your CaDDN achieves 5% AP at 0.7 IoU whereas MonoJSG is barely 1% AP at the same threshold. I believe both of your methods used the same setting, i.e., front cameras and vehicle class.
Do you have any ideas for where the difference might come from?
Thanks a lot!
The text was updated successfully, but these errors were encountered: