Open xmyqsh opened 4 years ago
Guess what? Benchmark trainset
car AP@0.70, 0.70, 0.70:
bbox AP:98.16, 91.20, 90.32
bev AP:97.97, 90.36, 89.80
3d AP:90.34, 88.32, 80.83
aos AP:98.09, 90.94, 89.95
with RGB feature trainset
car AP@0.70, 0.70, 0.70:
bbox AP:98.32, 91.37, 91.30
bev AP:98.23, 90.55, 90.13
3d AP:97.91, 89.30, 86.74
aos AP:98.14, 91.09, 90.88
Hi, thanks for sharing your results. What is the difference in experimental setup between the results in your first post and the results in your follow-up post?
Did you disable database sampling at training time? Otherwise the pasted samples may have inconsistent RGB features.
Wondering how to implement seg feature on this framework quickly. I try to implement while in pipelines/preprocess.py file, however it's seen like i can't inference image in subprecessor. How to modify it? Thanks.
Here is a simple experiment on KITTI dataset. By adding RGB features into points, the 3d AP increases, but the bev AP drops a lot.
Benchmark
with RGB feature
Based on Painted PointPillars result with segmentation feature instead of RGB feature BEV on test set
I address this as an overfitting problem and will test it.
Does anybody observe a similar result? How about using the Nucense dataset? How about adding augmentation on RGB?
Hope for large 3d AP gain on Pedestrian and Cyclist.